{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Session. Variational Autoencoders\n",
    "\n",
    "During this practical session you will implement a vanilla variational autoencoder on MNIST and then a modification of VAE that can be used for semi-supervised learning. To emphasize the probabilistic nature of the models, both implementations will be based on classes for parametric probabilistic distributions that generally follow the design of *tensorflow.distributions*, *torch.distributions*.\n",
    "\n",
    "To complete the notebook you will have to implement several classes for distributions and then use them to construct the stochastic graph for loss function computation and then train the models.\n",
    "\n",
    "# AEs  vs. VAEs\n",
    "\n",
    "Although autoencoders can provide good reconstruction quality, \n",
    "\n",
    "![Autoencoder reconstructions](ae_reconstructions.png)\n",
    "\n",
    "the model has no control over the learned latent representations. For example, an interpolation of latent representaitons of two digit is typically not a latent representation for a digit:\n",
    "\n",
    "![Autoencoder interpolations](ae_interpolations.png)\n",
    "\n",
    "On the other hand, a standard VAE model forces latent representation to fit multivariate Gaussian distribution. As a result, an interpolation of two latent representations is likely to be a latent representation of a digit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you are using google collab install the libraries with \n",
    "# !pip3 install torch\n",
    "# !pip3 install torchvision\n",
    "from torchvision.datasets import MNIST\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the semi-supervised learning task we remove 95% of labels from the training set. In the modified training set the observed labels have a standard one-hot encoding and the unobserved labels are represented by all-zero ten dimensional vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = MNIST('mnist', download=True, train=True)\n",
    "new_train_labels = torch.zeros(60000, 10)\n",
    "observed = np.random.choice(60000, 3000)\n",
    "new_train_labels[observed] = torch.eye(10)[data.train_labels][observed]\n",
    "train_data = TensorDataset(data.train_data.view(-1, 28 * 28).float() / 255,\n",
    "                           new_train_labels)\n",
    "\n",
    "data = MNIST('mnist', download=True, train=False)\n",
    "test_data = TensorDataset(data.test_data.view(-1, 28 * 28).float() / 255,\n",
    "                          data.test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distributions for VAE\n",
    "\n",
    "To define the probabilistic model for VAE we need two types of distributions. For latent variables $z$ we need a multivariate normal distribution with diagonal covariance matrix (to put other way, a vector of independent normal random variables). For observarions $x$ we need a vector of independent Bernoulli random variables.\n",
    "\n",
    "All the distributions we need are already implemented in *torch.distributions* and can be easily used in practice. At this practical session, for educational purposes, we suggest to implement it yourself.\n",
    "\n",
    "## Bernoulli random vector\n",
    "\n",
    "It is a good practice to store the distribution parameters as logits $l(x=1)$, because it helps to avoid computation of logarithms of very small values. Logits are converted to probabilities with sigmoid function $p(x=1) = \\frac{1}{1 + \\exp(-l(x = 1))}$.\n",
    "\n",
    "### Class details\n",
    "\n",
    "- *self.logits* is a $N \\times D$-dimensional tensor of logits for each of $D$ pixels of $N$ object in batch.\n",
    "\n",
    "- *log_prob(x)* takes a tensor of observations of size $N \\times D$ (batch size $\\times$ vector dimensionality). Returns an $n$-dimensional vector of logarithms of observation probabilisties for each object in the batch:\n",
    "\n",
    "\\begin{equation}\n",
    "\\sum_{d = 1}^{D} \\left( x_{nd} \\log p(x_{nd} = 1) + (1 - x_{nd}) \\log p(x_{nd} = 0) \\right), \\enspace n=1, \\dots, N\n",
    "\\end{equation}\n",
    "\n",
    "- *sample( )* returns a $N \\times D$ binary vector sampled from the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to implement\n",
    "class BernoulliVector():\n",
    "    def __init__(self, logits):\n",
    "        self.logits = logits\n",
    "\n",
    "    def log_prob(self, x):\n",
    "        pixelwise_log_probs = (\n",
    "            x * (self.logits - nn.functional.softplus(self.logits))\n",
    "            - (1 - x) * nn.functional.softplus(self.logits)\n",
    "        )\n",
    "        return pixelwise_log_probs.sum(1)\n",
    "    \n",
    "    def sample(self):\n",
    "        # torch.rand_like\n",
    "        samples = (\n",
    "            nn.functional.sigmoid(self.logits)\n",
    "            >= torch.rand_like(self.logits))\n",
    "        return samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'torch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-b6e64a49d731>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"All fine!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mtest_BernoulliVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-b6e64a49d731>\u001b[0m in \u001b[0;36mtest_BernoulliVector\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# you can check your implementation with the following test:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_BernoulliVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     logits = torch.tensor([[ 0.26257313,  1.00010365,  1.32164169, -0.60049884,  0.47478581],\n\u001b[0m\u001b[1;32m      4\u001b[0m                            \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.69943423\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m0.40572153\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m0.91215638\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m1.36048238\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;36m0.28434441\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                            [ 0.11055949, -0.65058279, -1.74598369,  1.2715774 , -0.60143489]])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
     ]
    }
   ],
   "source": [
    "# you can check your implementation with the following test:\n",
    "def test_BernoulliVector():\n",
    "    logits = torch.tensor([[ 0.26257313,  1.00010365,  1.32164169, -0.60049884,  0.47478581],\n",
    "                           [-0.69943423,  0.40572153,  0.91215638,  1.36048238,  0.28434441],\n",
    "                           [ 0.11055949, -0.65058279, -1.74598369,  1.2715774 , -0.60143489]])\n",
    "    bv = BernoulliVector(logits)\n",
    "    # test log_prob()\n",
    "    x = torch.tensor([[0, 1, 1, 0, 1],\n",
    "                      [0, 0, 0, 0, 0],\n",
    "                      [0, 1, 1, 0, 1]], dtype=torch.float32)\n",
    "    log_probs = bv.log_prob(x)\n",
    "    assert(log_probs.shape[0] == 3), 'log_prob() returns wrong shape'\n",
    "    \n",
    "    true_log_probs = np.asarray([-2.3037, -5.0039, -6.2844], dtype=np.float32)\n",
    "    np.testing.assert_allclose(log_probs.numpy(), true_log_probs, atol=1e-4,\n",
    "                               err_msg='log_prob() returns wrong values')\n",
    "    # test sample()\n",
    "    assert(logits.shape == bv.sample().shape), 'sample() returns wrong shape'\n",
    "    \n",
    "    mean = torch.zeros_like(logits)\n",
    "    for i in range(1024):\n",
    "        mean += 1 / 1024 * bv.sample().type(torch.float32)\n",
    "        \n",
    "    np.testing.assert_allclose(nn.functional.sigmoid(logits).numpy(), mean, atol=1e-1,\n",
    "                               err_msg='law of large number seems to be violated by sample()')\n",
    "    \n",
    "    print(\"All fine!\")\n",
    "    \n",
    "test_BernoulliVector()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Normal Distribution\n",
    "\n",
    "### Class details\n",
    "\n",
    "- *self.loc, self.scale* are $N\\times d$-dimensional tensors that store mean and standard deviation for $d$ components of the latent representation for $N$ object in the batch\n",
    "\n",
    "- *log_prob(z)* takes a $N \\times d$-dimensional tensor and returns log-density for each object in the batch\n",
    "\\begin{equation}\n",
    "\\sum_{d=1}^D \\log \\mathcal{N}(z_{nd} \\mid \\mu_{nd}, \\sigma_{nd}), \\enspace n=1, \\dots, N\n",
    "\\end{equation}\n",
    "- *sample( )* returns a $N \\times d$-dimensional tensor samples from the distribution. **For the reparametrization trick to work properly, the sampling procedure has to be a deterministic function $f$ of a random tensor $\\epsilon$ and the model parameters $\\theta$: $z = f(\\epsilon, \\theta)$**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to implement\n",
    "class MultivariateNormalDiag():\n",
    "    def __init__(self, loc=None, scale=None):\n",
    "        self.loc = loc\n",
    "        self.scale = scale\n",
    "        \n",
    "    def log_prob(self, z):\n",
    "        normalization_constant = (\n",
    "            - self.scale.log()\n",
    "            - 0.5 * np.log(2 * np.pi))\n",
    "        square_term = -0.5 * ((z - self.loc) / self.scale) ** 2\n",
    "        log_prob_vec = normalization_constant + square_term\n",
    "        return log_prob_vec.sum(1)\n",
    "        \n",
    "    def sample(self):\n",
    "        # torch.randn_like\n",
    "        z = self.loc + self.scale * torch.randn_like(self.scale)\n",
    "        return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_MultivariateNormalDiag():\n",
    "    mean = torch.tensor([[ 0.0619,  1.9728,  0.2092],\n",
    "                         [ 0.3971, -0.1817,  1.1508]])\n",
    "    scale = torch.tensor([[ 0.0619, 1.9728,  0.2092],\n",
    "                          [ 0.3971, 0.1817,  1.1508]])\n",
    "    mnd = MultivariateNormalDiag(mean, scale)\n",
    "    # test log_prob()\n",
    "    x = torch.ones_like(mean)\n",
    "    log_probs = mnd.log_prob(x)\n",
    "    assert(log_probs.shape[0] == 2), 'log_prob() returns wrong shape'\n",
    "    \n",
    "    true_log_probs = np.asarray([-121.1941,  -22.5777], dtype=np.float32)\n",
    "    np.testing.assert_allclose(log_probs.numpy(), true_log_probs, atol=1e-4,\n",
    "                               err_msg='log_prob() returns wrong values')\n",
    "    # test sample()\n",
    "    assert(mean.shape == mnd.sample().shape), 'sample() returns wrong shape'\n",
    "    \n",
    "    est_mean = torch.zeros_like(mean)\n",
    "    for i in range(1024):\n",
    "        est_mean += 1 / 1024 * mnd.sample()\n",
    "        \n",
    "    np.testing.assert_allclose(est_mean, mean, atol=1e-1,\n",
    "                               err_msg='law of large number seems to be violated by sample()')\n",
    "    \n",
    "    print(\"All fine!\")\n",
    "    \n",
    "test_MultivariateNormalDiag()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vanilla VAE\n",
    "\n",
    "## A brief VAE description\n",
    "\n",
    "A variational autoencoder consists of two components. The first is a probabilistic model for observations: \n",
    "\\begin{align}\n",
    "& p(x, z \\mid \\theta) =  p(z) p(x \\mid z, \\theta) \\\\\n",
    "& p(z) = \\mathcal N(z \\mid 0, I) \\\\\n",
    "& p(x \\mid z, \\theta) = \\prod_{i = 1}^D p_i(z, \\theta)^{x_i} (1 - p_i(z, \\theta))^{1 - x_i}.\n",
    "\\end{align}\n",
    "The second is a variational approximation, used to compute the lower bound on marginal likelihood (our loss function)\n",
    "\\begin{equation}\n",
    "q(z \\mid x, \\phi) = \\mathcal N(z \\mid \\mu(x, \\phi), \\operatorname{diag}(\\sigma^2(x, \\phi)))\n",
    "\\end{equation}\n",
    "\n",
    "The lower bound for one object $x$ from the minibatch has form\n",
    "$$ \\mathcal L(x, \\theta, \\phi) = \\mathbb E_{q(z \\mid x, \\phi)} \\left[ \\log p(x \\mid z, \\phi) + \\log p(z) - \\log q(z \\mid x, \\theta) \\right] $$\n",
    "\n",
    "In practice, we can't compute the above expectation. Therefore, we approximate it with the following one-sample Monte-Carlo estimate:\n",
    "\\begin{align*}\n",
    "\\log p(x \\mid z_0, \\phi) + \\log p(z_0) - \\log q(z_0 \\mid x, \\theta) \\\\\n",
    "z_0 = \\mu(x, \\phi) + \\sigma^2(x, \\phi)^T \\varepsilon_0 \\\\\n",
    "\\varepsilon_0 \\sim \\mathcal N(0, I)\n",
    "\\end{align*}\n",
    "**Note that this choice of the Monte-Carlo estimate for expectation is crucial and is typically reffered to as reparametrization trick.** For more details see [Auto-encoding Variational Bayes](https://arxiv.org/abs/1312.6114) paper.\n",
    "\n",
    "Finally, to train the model we average the lower bound values over the minibatch and then maximize the average with gradient ascent:\n",
    "\n",
    "$$ \\frac{1}{N} \\sum_{n=1}^N \\log p(x_n \\mid z_n, \\phi) + \\log p(z_n) - \\log q(z_n \\mid x_n, \\theta) \\rightarrow \\max_{\\theta, \\phi} $$\n",
    "\n",
    "## Encoder and decoder\n",
    "\n",
    "$q(z\\mid x, \\theta)$ is usually referred to as encoder and $p(x \\mid z, \\phi)$ is usually reffered to as decoder. To parametrize these distributions we introduce two neural networks:\n",
    "\n",
    "- *enc* takes $x$ as input and return $2 \\times d$-dimensional vector to parametrize mean and standard deviation of $q(z \\mid x, \\theta)$\n",
    "- *dec* takes a latent representation $z$ and returns the logits of distribution $p(x \\mid z, \\phi)$.\n",
    "\n",
    "The computational graph has a simple structure of autoencoder. The only difference is that now it uses a stochastic variable $\\varepsilon$:\n",
    "\n",
    "![vae](vae.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "d, nh, D = 32, 100, 28 * 28\n",
    "\n",
    "enc = nn.Sequential(\n",
    "    nn.Linear(D, nh),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(nh, nh),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(nh, 2 * d)) # mind the non-linearities at the final layer\n",
    "\n",
    "dec = nn.Sequential(\n",
    "    nn.Linear(d, nh),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(nh, nh),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(nh, D)) # <-----------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss function\n",
    "\n",
    "Implement the loss function for the variational autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to implement\n",
    "def loss(x, encoder, decoder):\n",
    "    # encoder\n",
    "    q_loc_scale = encoder(x)\n",
    "    qz_x = MultivariateNormalDiag(q_loc_scale[:, :d],\n",
    "                                  nn.functional.softplus(q_loc_scale[:, d:]))\n",
    "    z = qz_x.sample()\n",
    "    pz = MultivariateNormalDiag(torch.zeros_like(z), torch.ones_like(z))\n",
    "    px_z_logits = decoder(z)\n",
    "    px_z = BernoulliVector(px_z_logits)\n",
    "    return (px_z.log_prob(x) + pz.log_prob(z) - qz_x.log_prob(z)).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "def train_model(encoder, decoder, batch_size=100, num_epochs=3, learning_rate=1e-3):\n",
    "    gd = optim.Adam(chain(encoder.parameters(), decoder.parameters()), lr=learning_rate)\n",
    "    dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "    test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False)\n",
    "    train_losses = []\n",
    "    test_results = []\n",
    "    for _ in range(num_epochs):\n",
    "        for i, (batch, _) in enumerate(dataloader):\n",
    "            total = len(dataloader)\n",
    "            loss_value = loss(batch, encoder, decoder)\n",
    "            (-loss_value).backward()\n",
    "            train_losses.append(loss_value.cpu().item())\n",
    "            if (i + 1) % 10 == 0:\n",
    "                print('\\rTrain loss:', train_losses[-1],\n",
    "                      'Batch', i + 1, 'of', total, ' ' * 10, end='', flush=True)\n",
    "            gd.step()\n",
    "            gd.zero_grad()\n",
    "        test_elbo = 0.\n",
    "        for i, (batch, _) in enumerate(test_dataloader):\n",
    "            batch_elbo = loss(batch, encoder, decoder)\n",
    "            test_elbo += (batch_elbo - test_elbo) / (i + 1)\n",
    "        print('\\nTest loss after an epoch: {}'.format(test_elbo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# my implementation has test loss = -110.31\n",
    "train_model(enc, dec, num_epochs=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisations\n",
    "\n",
    "- How do reconstruction compare to reconstructions of autoencoder?\n",
    "- Interpolations?\n",
    "- Is the latent space regularly covered? \n",
    "- Is there any dependence between T-SNE encoding and the digit label?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_reconstructions(encoder, decoder):\n",
    "    batch = test_data[np.random.choice(10000, 25)][0]\n",
    "    rec = nn.functional.sigmoid(decoder(encoder(batch)[:, :d]))\n",
    "    rec = rec.view(-1, 28, 28).data\n",
    "    batch = batch.view(-1, 28, 28).numpy()\n",
    "    \n",
    "    fig, axes = plt.subplots(nrows=5, ncols=10, figsize=(14, 7),\n",
    "                             subplot_kw={'xticks': [], 'yticks': []})\n",
    "    for i in range(25):\n",
    "        axes[i % 5, 2 * (i // 5)].imshow(batch[i], cmap='gray')\n",
    "        axes[i % 5, 2 * (i // 5) + 1].imshow(rec[i], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_reconstructions(enc, dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_interpolations(encoder, decoder):\n",
    "    batch = encoder(test_data[np.random.choice(10000, 10)][0])\n",
    "    z_0 = batch[:5, :d].view(5, 1, d)\n",
    "    z_1 = batch[5:, :d].view(5, 1, d)\n",
    "    alpha = torch.tensor(np.linspace(0., 1., 10), dtype=torch.float32)\n",
    "    alpha = alpha.view(1, 10, 1)\n",
    "    interpolations_z = (z_0 * alpha + z_1 * (1 - alpha))\n",
    "    interpolations_z = interpolations_z.view(50, d)\n",
    "    interpolations_x = nn.functional.sigmoid(decoder(interpolations_z))\n",
    "    interpolations_x = interpolations_x.view(5, 10, 28, 28).data.numpy()\n",
    "    \n",
    "    fig, axes = plt.subplots(nrows=5, ncols=10, figsize=(14, 7),\n",
    "                             subplot_kw={'xticks': [], 'yticks': []})\n",
    "    for i in range(50):\n",
    "        axes[i // 10, i % 10].imshow(interpolations_x[i // 10, i % 10], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wr9hQ7qTXRS3SQ82MR42wOTTFKyNJW2yxhaTiTfCWrOhHzqpr1K1dqr2vMUDURyrRtbvuuqt5rOtZ9XzfE044QVL9QENa+qKrt66clkiIrzFcn7dMxH7I5/XDpzgoEo+z2xZ60iqZNrTS3PVb04BfF5oSJfY1kSgJ89Xti/2A2sOa7W0qkHlAy16prId4+2uHC1Nfs6m2TWU+UjNI21SpRJLYA7x2hvlMbc0YvocsFkQ9/GgHvusxD/37CvOU9vpj+R6yWBBJP/zww5vHmM/oV6vvQr+hrHWJiIQQQgghhBB6JzciIYQQQgghhN7ppVid0C0pMVIp/CXtRSphJsJFflIpqVykGXmaAmHP2im5pMwQkvIUp6GEpeaCsCRpVdLcJ6RToOQtZik83G677SS1i7RJQaLo9ZprrmnGaGlJOHSMhWGEbr2BAekY3paWdpXeIAFI++A5nqZAShw26Clg3ZSasYWRmR+eVsAcvuyyy5rHSLkktcNPcuU1sD2HlC/mubenHcPcXB/8Okh79MJy2mxji6RBSsUW+VlrYwm1RhLTRi1V44UXXpBUCvv9pGXsCf08RYH20Og4trm5MSBtiNRKT+/rprx5+hXp0bW25JsSpPyx13ozBGwU3Vw/UlK7jWI2JbA9tKs14uCna0favTdI2BRhL2Cv5buJNFs/n5+k9Hoq6hBIRCSEEEIIIYTQO4saEeFOjMLASy+9tBmrRT0okMN77Z6CbjGnex8ojuXO2VuE0tbss5/9rKT2nTR3jEP2SOBt9igPXgRvOYk3unuIpFQKOCk89GgJB4DhkfADwfAkfvnLX5bU9sKOxQvGZ+wHH6GlN08gUkahnLfo7UabXFt0x4ZqXkWiTWPzWvMePQLEIZcelWMcb/ROO+3UjNHGkmuvzXeiJR4dJTLgn9EY8c+c9QkvvlQKVtHQC16XLVsmqaxZbpPYIvPdo0m1QxSnCY9soOXatWslSTvvvHMzhn2hrf9dVyNvQLGpFF93i6S9KUU3IuL7JpE91oexNYDZWGBD2JnrQESEnx7p9PnvryONY1/YGHDNtHX3IwUALdx7T0RkU9HplUA/vuv6dzrWMp7j+/cTTzwhaXjf3xIRCSGEEEIIIfROLxERvJp+YBmeVWpFpNm54p6bzp0zz/FD0/bbbz9JxZvt+ejcaZPb7rUW3QP7hkQ3H9q9KJdffrmkdq45tTNd771UvNJEOFwfDrEiquT5/Xi3+Ny8fmQM0SSp2KBHc4hQeN0IngK0cfvafvvtJZVaG/4tFW35eeKJJzZj2Bf/DzYvjctzWKtz8EP5mN+1nHG8y9in2yUREKKWzGOpeBD5f9xLPXSbeyX4zN3Dh02wTnlEkja/tdouopzYHWufVOxtyOvbfKgdrIqmtQgatoq2PsY6x37i9WLdAyGnCfe+E+3otqH1x9DXIyLMc2pLPJo0pnVtPrh+tCJnr/A9A22xIW+Lz3cQtNpUIkquHd9F9txzT0ntKDoZBzzfteO7CN8NXbuhefkXE/RbsWKFpHaNCGsb2vi6RzYIe8NQonGJiIQQQgghhBB6JzciIYQQQgghhN7p5WT1WtERoaEXX3yxeYz0oFqbRk4QJuzkqQi0mf21X/s1SSXcJ5Vi4nPOOUdSu73jVVddJWkcBbGeptZNmZJK6Jx0Dg+Xr169WlIJHZOOJZVWgocddpik9snES5culSSde+65rdeWSqrbWFr6etgWLb2VczcVwcfQmZPYvaiT51E05qk1J510Uuv53vKWz2sM4WQP2dZS3fidOe0paMx5fnqhP7qQEsJJ4v4apGT5a2JzY0udqZ0WT/icdc1TM7hmxjx1Bl1IbfXUBpqD1Npfjk0zhznq6QSk+tFsw+2LlA4am/gaypq/9dZbS2pry9hY0k83BE9l6Z4Izv4rFa1Ypzw1GPvCLj2tjbk5hnVtPvi+ylp/3HHHSSqpu1K5fmyQImuptJpGN39N/m6abA78OkkpPe200yS1bQ+Yh3xHkaT77rtPUn0t4Pdp1E5q67f33ntLKt/NXD/mOHOYdtuSdNttt0kankaJiIQQQgghhBB6p5cDDaF2F+ZeKjyAeArcq9L1tPhrc8fHXaG3cOS1KB5zr+uqVas26Hr6ZH1acLqns+s99btnPNUc4kXLUEnaY489JBVP4hFHHNGMUYyHp4eiYqlER4YaEenqty7PCd55bwvdHaOg3aNqtFhFK7c9Pgtai3oLZjxlQ/Yc1mwQu3LPKqCde0g54BHPqtss9kSkziOZ/N/8nRcs8tjQvDo1XEOKe71gH83w6HsjBFpB06bbo5WsZxRie7EiUTrWRW8pPdZoklRszlt9MseICNHUxCEaVyvM5DW9NTLF/kT6amvc2PRj3nozErIKiKq5V5Xnsz97FJjGAIz5mjnXQZs1umvxUIpnu3QPL5SKzaAjRwtI5TrQw1vtEx1hX/a9umtrQ9JgvjDH2COlEk2qHceAdtgX31uk0mgGXYdqLxuTmn7MWb63sR9Isw8S96ZG6De0722JiIQQQgghhBB6p5eICHd07kXFK+h3Zt12gB4t4e6u1t6OOhMOrXOva/fQIf4Pfw9Duauuee27h9NI5e7XvU14CfFGuwcZzws//XPgeslbdX35v6mNcE83v/tnNGmPhGvENdb0QyvXAW8gHi/Xj9/xxHv9CDoceeSRktoeR37fa6+9JLXz13lfrvek9QPeWy36gefKvdLk++LVr7VQxb5cV/QgKuceWeYmByd6PRQebtdrKNp17c69nXi0PLKxzz77SCrRoNqcpt2ne72Yk0QC/PMgAkJev3uzef2hH65Z2zOwFz/48vDDD5ckHXDAAbPGmH/kmPtc43faSpN7LhUt2UfWFZ3u6jfp/aSWP4923mL2hBNOkFRqRHyM56OL12gREWEvncu7ur6Haw7xEE5/T6x7rtGpp54qqUQjaakqlfWL6Dnrn1S+p7B31mq45rKbSdvX+lD7vuJRR9rc77bbbpLa3n7WTGqRfO3vajfN7Y6B7xheF41+RIB9b2DNZB+48847m7Fu7eVQ7CcRkRBCCCGEEELv5EYkhBBCCCGE0DuLmppFiI0wpYePCOt6SgFhIv7Ow0aeAtSF16Wo2lOI+H8I4fmJ0EMLTzlcA9q5TjzmBV7d53lKA6ky3VQ5qaQieREe8DlQGOYtHIfe2rKrh4d+GfNQOkVznLTsxYUUr3Y/E6mkyJBWVEv9QytPkRmabh5K5zpJz3DbwOZIaZFKC1RStEglkkraJHPTw/OkrFG46GltvB809CLYoWnnMGfQzsPp2BZpRFIpOqSlNmkvUkljI/zu2tG+sZbih02SfjWm9AU+d+aRpzNiQ8cee2zzGCltND7wFqrYCbbq6TFoOVfxZq2V6lwpRENpH1or6icdkJOYpTL/ai2Mu81hvHU7ezGpM6T8+vNrRepD0Wdd8D79ewTfMWhOIhWbQ1vXm/WKVJjrrruuGeMx9pUNTc8dun5SPU3aW4zT1IX57fsm38uYr5dcckkzRlo0NjgGLeZDLf2O1Gep7Ansj/6djrlLkf///u//NmNDPTYgEZEQQgghhBBC7yxqRIQ7OTydHPomFQ+1ewy5K8aDf8cddzRj3CVzJ+x30BS74ilzzyF/xwF8XpToBywOgVrxLV4qvM1S0cy9f+iJd4WWsVLRgOt1zzMHGVJw54W03D3T7tc93XNFqIYA+nE9rh8tUv3wQbzVeJi95R3eBiIaeLGlUqTOY+4VQy+8YXjApOG1Ua3ZHjblnhg80N6mmHE8iF7QibeV1/QW0BQqYs/uGaTIkwOYPCIy5EO/eE/Ygc9RCqsPPfTQ5jF0RLtaow2u1wuxicAxp71BAHOfaJTrOpcnbEge625ETCpacdCqVDRlzff5x7USZfJ1D88qc7p22G4tklRrH/9K710aTvMT7JC9UpIOOeQQScWu3KvKfMOr6pkEPIZ26+vRX58C7CHYHvh7wXPvXn2imIz5GkXU48orr5Qk3X333c0YLc2HtgcsFuyprh3fBZnf/n0CW/uv//ovSe29mHVu2jWrRWFrey7a+r5Be+i//du/lVSOHZCG17YXEhEJIYQQQggh9E4vERHu6Lw951lnnSWp7T3FM8odsR9OhZePO2ePrpx22mmSpIMOOkhSO7cTz8RVV10lSbrnnnuasSHnTuN5w0vlntWTTjpJUj1PH++fe76IAqGd31njFSNK4HfWtM279NJLJbXrJoaWY9il+/48inH00UdLKrn0UomO4NVy/cjpJwrnEQJ0wyvmrX1vuOEGSdI111wjadg1Ig7aYQuuHfbiNoQd4p0hkiYVD1atZoJ6EzSnzapUbI7IiHtyhmx7fK5o51E3WvXusssuzWNoR0TO5x9zH7tzjzXRNbyF2JhUDs7E3ta3zfEQbJL3wGfsdQvMNd8XiNoREfE1nSgHr0GESCp2tXr1aknFSy2tX+1gbax2KN8k4H141gD7B/uENPugNPfos47RLtXnJh5W5vnG2EeHYHtQey+sVZ4xwJpUa5nP9wz2AHSUypo45HVsobiG6ON1mmjHXPFsgSuuuEJS2QNc12nW7JVgX/Ua624Nl+tHJAkdh569IiUiEkIIIYQQQpgAuREJIYQQQggh9M6ipmYRNiIUSbs/qYSbKPiSSnidxw4++OBmzNNapHaIlCJEQtGcKCmVlAVamHl7zCGFg7ugHWFJLxYkLLfddts1j5EuxN9RCCzNLr6stf3lNR966KFm7MILL5RUCoa9IHbI2kmzW+ZSYCkVGyBVRippg6QreAoDrVK76XJSCXuSuuANFr785S9LKp9d7QTdoeDpJGjH500ai1TmJmkyUgkZ89PTaVwrqR0mppgfzbzF5eWXXy6p2P+Q0ygdbIQ5580xbrnlFkntomlSs/g7L85mPcOGPX3oxhtvlFRaW9JQQiq2OOSi/nXBe/dUR9JzPU2VdY813+2Nx/gM3L74ndQ2TyfsFnRuqH6T1ru770qlFer111/fPMZcJuXWU7PQeuXKlZLapzOja63YetLXvjHxNYfUl5tuuql5jFTwWlouujEvN7X0otrRC6RBStIJJ5wgqaRteWvt888/X1LZN8ey9i8WXD8pt1LRi/XO95nzzjtPUntNGzqJiIQQQgghhBB6Z1EjIsAdsd/1cnfn7TyJcnDQnBe2dotA3buMZwZP4GWXXdaM/c///I+kcsc41PZlrwSeVdfu5ptvltRuU4w3msJiDuiTyjXjIXPvDFEqvN4XX3xxM3b11VdLGqdnAnvhWjm4TJJWrVolqR0FoCUohbB+kBqvhX4encLm7rrrrtZrS7O9rUP2hNU8mRT7eqEqBXC0CJRKC2ha+3q0knnKYZjeLIJmCGjn0Ti8/3hpx+Jp5X3yvr3lNVEeWolL0q233iqp2J+vedguz+e5UikYZswjxnxuQz90dC547+7VowWqR7yxmeXLl0tqzzGioHj377333mYMbdHN/27MkSTHo4/MYTz1UpljBx54oKT2nKbIGn29GLbbUGBa8esj2sHeK0n/+I//KKm0vnevNM1xaJYztu8dGxPmmrcw/od/+AdJJdvA1zZsbwxF1n2ADr4//v3f/72kEtX0SCd2OKb5mYhICCGEEEIIoXdyIxJCCCGEEELonSUbEn5esmTJBsWqSX2hoMbP9yCFiKIlqYTpOLfBixL5W3rBk9bhj1G46ekfpNFszPSYmZmZDW4Uv1DtvCc86Rt+1gVnYlCA7cXWFKdTlEgqjFRC7qTKEUqWimakh22kUN/qmZmZZRv6RwvVj+YIUimm9jNsSAekCJtzHaSiPWlsbnuEndHWC0RJz9mYJ+j2YXuAdp7Cxjz0hgf8jmZ+ujUpg9iSN6wg5EwqkadbdtOKNlKazAbb3ny1q50lUZvLFKejmV8nNosd1U6X7/aT777GRqSXebuO15LULkhHS+zSU2B4PnZV06jH9KvebG8dr9n8jo78rOkzkPSOidveK7x+6+dQi/b73DPm8f9IGpZeHQZpe2NhfW0vEZEQQgghhBBC7yxqRKTy983vXW9MDR/r3jnXCqdrXpzFuNOehIfBtet6Ypza6b5d7ebSpAcP2EQ8DDWt/LGuJht6OnJfHtYxeLfmSw9esUF4pTd2MpBpAAAgAElEQVSUgXgN4xlcGKO0vYEQ21sAQ94zRkBsbwEkIhJCCCGEEEIYLL207wX36BHRGFNL2Eky1PzTsVDTbC4do/GGE80Wh+gaQghhWklEJIQQQgghhNA7uREJIYQQQggh9E5uREIIIYQQQgi9kxuREEIIIYQQQu9saLH6c5IeW4w3MiK2n+ffRbuXiX7zJ9otjPnoF+1eJra3MGJ78ye2N3+i3cKIfvNnvbXboHNEQgghhBBCCGFjkNSsEEIIIYQQQu/kRiSEEEIIIYTQO7kRCSGEEEIIIfRObkRCCCGEEEIIvZMbkRBCCCGEEELv5EYkhBBCCCGE0Du5EQkhhBBCCCH0Tm5EQgghhBBCCL2TG5EQQgghhBBC7+RGJIQQQgghhNA7uREJIYQQQggh9E5uREIIIYQQQgi985oNefKSJUtmFuuNjImZmZklG/o30a7huZmZmS029I+i38vE9hbEBttetGvIvF0Ysb35E9tbANkzFkRsbwGsr+0lIhL65rFJv4GwyRLbmz/RbmFEv/kT7cKkiO31QG5EQgghhBBCCL2TG5EQQgghhBBC7+RGJIQQQgghhNA7uREJIYQQQggh9M4Gdc3qmyVLZhfc1x771a9+1cfbGRXRbuPR1c3/Hf3mJtrNn9p8hZmZNGXZUFzP6BdCCMMgEZEQQgghhBBC70w8IvLa1762+X2rrbaSJJ1wwgmSpIMPPrgZ23HHHSVJL7zwgiTpM5/5TDN27733SpK+853vSJJ++ctfNmPT6PnCs/e6172ueWzrrbeWJB1//PGSpIMOOqgZ23333SUVXb74xS82YzfeeKMk6cEHH5Qk/fSnP23GXMdpAv3e+MY3No+h33HHHSdJOvDAA5uxnXbaSVLR+9JLL23GVq1aJUm68847JUnf+973mrGf//znkqbLBl/1qpd9F29+85ubx7bZZhtJ0lFHHSVJWrZsWTO23XbbtZ6/evXqZuy6666TJN16662SpGeeeaYZ+8lPfiJpumywpt2WW24pSTr00EMlScuXL2/G3vOe90iS3v72t0uSHnrooWbs5ptvliTdcccdksr8laTvfve7kor9TUvkiXn7hje8oXnsne98pyRp7733llR0lMqe8a53vUuS9Pzzzzdj9913nyRp7dq1kqR77rmnGXv66aclST/+8Y8ltW1wrFqi3WteU7Z87HD77beXJO2zzz7N2G677SapzG1fw5599llJ0gMPPCCprd3DDz8sSfr+978vSfrZz37WjNW0G9va+OpXv7r5ne8u2CA6SmXPeN/73td6jlTsCjvDFqWiJXOYdVAqduiajUk/j0iyFr7pTW+SJL3jHe9oxlj30BMtpbIH8z0FG5TKXP72t78tSfrBD37QjP3iF7+Q1LbBsc5lafZ8fv3rX9+Mbb755pKkLbZ4+fgTt0vWwre97W2SpCeeeKIZY+5+61vfkiS9+OKLzRjzeLHWwkREQgghhBBCCL2TG5EQQgghhBBC70wsNYsQp4eNPvGJT0iSPvaxj0kqqVrOk08+KakdYr766qsllVQP0mSkdmh4WkC7HXbYoXns93//9yVJH/3oRyWVlA+HcC+pHpJ0yCGHSJIuueQSSe20o5deeknSuMK/6wMhdcLmkvTJT35SkvThD39YUglhSiUMSkiddCNJOvrooyUV/b70pS81Y9gqYeFpAO0In0tl3mJ7rh22SprQrrvu2oyRysW8/cIXvtCMrVmzRpL0ox/9SNJ0pGixZhEyl4pmv/M7vyOprStac+1Lly5txrA7UhO+/vWvN2MXXXSRpBJiR0OpzOUxzmn08zSOs846S5L0e7/3e5JKGqrUTuGS2qkEpFA+99xzkkqKpVTm8F133SWp6CiVz2Js6b+kwvjaf8QRR0iSPvKRj0iSjjzyyGbsLW95i6Rig56SxDqIXZESI0kXX3yxpNkpq/53viePQTup6LfZZps1j+2xxx6SpJNPPlmS9MEPfrAZ47vLW9/6Vknt1Bmun5+PPvpoM0aa6uWXXy5JuvLKK5sx0tLRUSo2PWQdaymVpPztu+++kqTf/u3fbsZ23nlnSWUtxBal2elAzF+p7Ldodv755zdjrJOeOj22tdC/85JahUbnnntuM7b//vtLKumVte+C2KOnr2FXt9xyiyTpggsuaMauuOIKSaX8QSppgxtDv0REQgghhBBCCL0z8YjILrvs0jx2+umnSyqFw3ghpFLY9Z//+Z+S2p4W7piJEDz++OPNGHfMY7nrXR+4M+aOVypeGfe2AkXAePq8wAsPGQVhFCxJReNpiyphexSzSsXDjLfV7eWpp56SVLxUrhH64eHxJgEUbOLJmgYbZE5uu+22zWMrVqyQVIrk3FuMdjRFcNtDa5oG4KGVircFG3RP2Fh1xDP47ne/u3kMu6OY1a+NNY8CVvc8Y3esfd7Yg9fAo+UNKMYcncP2PFJ+7LHHSpLe+973Smp77ilOpyDTi4LRDc+ir6Uf+tCHJBWPv3tR/fcxgXZcryQdfvjhkkqhv3usuXYKVn3N47WIfLo9n3HGGZLKGuAe60ceeWRjXMpEYO56owkilESSapFgshC8KBjPM3PePxNei6JsshKkkvnhBexjWAtrzXWIepx55pmS2t8D0ZG1yvcM9OQ5rvmee+4pqUStiMJL0r/9279JKnuyNA7tpHqjCb4jn3322ZLKXJbK92D0prmEVL6LECXx74vsKcccc4yk0khAKs1Q+PuNTSIiIYQQQgghhN6ZWESE3FNvt8gdGXlr119/fTP2F3/xF5LKnZnfzZInR76h58ThUZgmrz53ut4mFW80HgO8J5L0V3/1V5LqHik829wFe90JOZfcUY/Fg7Au0O+AAw5oHiOXlxxI1++f/umfJLU9M4AntqYfv+NFdQ/NWEE78lCl2dpdddVVzdhnP/tZScWb7zbEPKVWxPP7aSVK/rR7YsZqh6x5eO6k4plHO/JzpZLjzDro0Qy8qHjCPL+fSB/1d962dsw2iCfe6wqJprFGUW8kSZdddpmkUvfmnmS8pkQwTzzxxGYMr/dee+0lSbr77rubsbHllUM3iiGVCAj24esbbbbRzm0Izyz6nHrqqc0YkWHqJ3wtGEM9wyuBV9prRPgdL/ttt93WjLHXUq/gWRo//OEPJZV5ShRJKtEp6hBr+f1jbTvrdTJox/cybz9+0003SSprIcczSGWdZA0geimV75Ld+gmp2OxYtZPqrbfZU/jeJ5XsAyLpnj1EhgL2dc455zRjZDagn0ee+bx8D9qY8zgRkRBCCCGEEELv5EYkhBBCCCGE0DsTS82iUMvTYyjwolXYpz71qWaM0CahNS9kJ1xEwZOHlCgSG3tqlp9KSrh2v/32ax6jOPAb3/iGJOnP/uzPmjGK1WvakTZE6M1PdqZ9JQWHY26h6vpRXOknCWMfK1eulCT95V/+ZTNG0TD6+Wths6R9eNEdBWUUyY41Lcavl+I2Ty+iGPraa6+VJH36059uxggFYzv+WmhOe1RfC0h5I5XET3kdE369pE96oT9pHaQh/PM//3MzRloaduOvRcop66inaaIZGnL6ur/GGNNjSCXy9BhOUWaOkQoolQJrL9YHUhrQwe2ZlErSHzwlwj+DMcE65e+fdY10lwsvvLAZI02LonW3F/TAdik8lkqqIZrXtBuj7fHePTWlmw5ISoxU0olqabl8FqS7+XrA3k4KrOvHvj02/WrakWpLCmCt1S7P8fnLa2G7rHFSScUiPbBms2NOzfL3jiakX3lKKnsuP71FL6Ax31H8d77DuO5ouVjfYRIRCSGEEEIIIfROrxER98TjefdD5fCaUuDlh6d072Tds0OxLN5B9yLg3cIrODZvArh2FFj63SwtATlYzz3Ic3kBKPbkp7cnPPDAAyWVwrsxR0RcPzx43p4OW/vyl78sqV2c2b1utz08q+AtMIm43HDDDZLanokx2aFrh8fOvVsUGn7+85+X1G7ZiXa168UzyJgXM+LdwjM4Vk+0v2/mmLeQvP322yWVVrtEL6Xifapp19XDD/3i/yHiNGYvYC366I0L8EKjIw02pPU7cIvXd/2wObzZtYjKWPF9gSgcUSU/WI+C6lokE1j7iPT5Y6yfc+3hYwRdpNKAA6++H3yJ7dTaZWPHrHfeQIDHWAe8UBs7HNPeIZX361kpjz32mKSS6eJtitkna953vtsxR/2ATmwP7Sh6l6ajhb7bEnOWeeaNOLBR9K5FhrBB//7C78xZ14+1YbHmcCIiIYQQQgghhN7JjUgIIYQQQgihd3pNzfLwLuFI0qmkEpYk/cNPye3iY4SPCdN5ahanN5NeMtb0Ik+PIX3KizYZ9xSDV6KmHWlKtTCe/99jxa+B3/2kV1LbsEHXoVtk6a/FGOkwnsZBuHSsaUVQKzD3sLmnE0lzzzG3vW543bUjlF4LL48J1w7NKACWSioRYfdaOgKv4XbHfCX11EPmpN+Q7uCfx1h1lIoteM98tCGdo1bYyjW77bGGUqTuaZroRtqDv+ZY04uwL08touCXVEFP7wA0dO1IhSZ1lz3WX4M0JU+5qaUpjQU+d08vIoWF+VWzPeas68c5DYcddpik9nkX2CqF8H4i+1hTBLkmX4ewC/Sp2V5NO9IA0c4L/Uktwq5JmfP/b4zrX+09M2f5jub7Rrcpke8bzF3OAfPGTuiM7VEiIZXC98Vi/N8wQwghhBBCCKNjYhERPFJ+t8cdLZ4Wv9ulPSPPxwMtlXaseFxo3yaVuzy8r3i+x4brVCv+okgaD58XsvuprlJbOyJT6IK3RipeBKIEY9WuC94XLxrG00I0zk+0xeOP7l7Qj87Yl3utKNr2CN3YQTsveiUKx3V6lBOvMtp5FI/TcfGoehEymo/Zi9oFr5VfJ/ONMS++pOAVXDsaLuy0006znsvrY99j9eJL7T2D63CvPuseY65fNyLpaxuRJPYaj1Ixh/F4j9UGXbvansF14TH1eQvsnz7GqcxHHXVU67WlohmefN8zxuiNhppXH5tj7fc9gz2TSJtnKtCg59hjj5XU9lizZzz00EOS2vN6rNkcUMsyQDvXB68989UbwKDdMcccI6m9txIppaWtNw8Ys3a1ttfd77VoJpXvdPz0iBJ7Ls2iPBLMukpr3/vvv78ZW+xGCYmIhBBCCCGEEHqnV1et303ROtDzfbm74+7VIxs8j7tDb3m31157SSqeavdKE2UZe52Da7dmzRpJpQWeVDwF3P0SJZKKZ7Dm+dpjjz0kFQ+ORwnIux6zNwHcK0wrOjwnkrTrrrtKKl4YPAdSsSG8L55XueOOO7ae47mueKbHHkly2+OaXDu88kSHfP6hFXPbo5xozZz2vyMiQuRlrN5Uf9/YATnMUpl36OL59t0IHJ5oqXgJsUn32lN3Ql7vmCMirh/rkM8n5h3zlfVMKl5W7NK1RTeiw97GEo8gbctd2zHZYe29+mPow6Fwbl/svUTevLV293V93nKIJG1nfT0ck3ZdWKM8ysQ+yl7rh2Lutttuksq+UqvLRDffc9mbqG/wsbHO41qNG9phZ0QmJenggw+WVDT0vyOiRwTTo6NE46666qrWv6XxRjUd14G9E9tDM0k64YQTJEm77767pLbNohfZLj53ib5xKLa3815s/cb97TyEEEIIIYQwSnIjEkIIIYQQQuidXlOzPLTIyZBeuEnBJqFLwsJSKdQkJWHZsmWvOOZhdtK2rr322o10FZPBtaOgjTZrUtEMDV07wngUJqGJNLvoyVvaEjYdc0gdXD/C3V7MRuoBtkNYWCppb7UUGdJESPXw/wctCWuOVUe/JlIt3PZI8aBgzu2LuUlajBdcY8fo4wXDfEaEjsealuAwR72lKddMkTW2JpW2nmjnc5N1k/nuKQp8Nrz2NGjneDoBc4oUQE+pJFWGRhSeotA9Nd31Y2+a63TsMeDXW0stYs1infd5S6pMrR08c5PUXd/DSYdhbtfaUY+RbnG1VPYM9gNaokrl+wl7ruuObqwDtROzSd8ca8teh2v3onNSIkldo/GBVGyP59e085R+uO222ySV9MBp0E6q68e83HvvvSVJZ599djOGHWKrtXUP2/VmCDfeeKMk6e6775bU3o8X+7tLIiIhhBBCCCGE3plYsTqeE28nCNwte9QDzz13he6VxlONR9Y9DLTBw7tIG+Du+xk6/l7xCvBTKlEPNHCPPnfIeKO32GKLZgw90dC1o1iJxgDuNRyTdlL7/XKn7/phX3ifPaqGpni33DPB6/KYe2GwNbwX/v+NFearz9vu/PPCTDz96OONDxjDO+P2VTt0coy43dU868xb5qS3YcSTjy3636NPbW52m1OMba6+ElyHt+xkbhERcf2wUS/4BbTk770QFC35/8aqX+19+3WyH9QiRuzPrJU+xhzGLj1qgtY8Z6zadakdSot+tUYvNIxAG9eBKBHrpLdQJSIy9ii6g825hqz9fNdzzzzRIPT1qFpXO4+w812PPXgamuxI5Vp9X6XYn+8r/r2DaBF7p0c22BvYj5n7knTXXXdJKutfn5HgRERCCCGEEEIIvTOxk9bwDtOmTpJOOeUUScXjRVtBqRyuwl2e30HzfO78vLUv+YZj96w61CWQCymVfGjuYmmpKhXt0Mm9p3gp8G655oceeqik0s5tWsCr4h4sPNN4FtyziqcFz797APGG4dnxHH/yN2knWDtgbAzUWqj6Y2jFHPMDH2kxjX35YZp4yvDKeFtkWqjefPPNG+kqJk/tM8fLVYteMG+xVz+QD43J73ebRMcx2dj6ULse7JE1zVt2EtElMuJty7stVz0Cij1Oi0dVqmvHWketAi13JWnt2rWtv/M9lfpDWiV7DRLe/WmpDelSi5zhgfbvMtQr8HxvHY3N0V61Fl1mj5+mOeyRTK6P74Few3vTTTdJKvulH1K67777SioZH/49ENvjNadJO6mtX/dQ5pUrVzZj1113naQyB31vIDsG/bzWhjWQx/rULxGREEIIIYQQQu/kRiSEEEIIIYTQOxNLzSJs5OlFd955p6QSkiM9Q5Kef/55SSVs5+3eCMV5+zwgvESxOgU5PjY2SBnw1CJSYLimxx9/vBmj+AsNSbmSStjusMMOk9Q+QZcUBU4P99O0x6qdVN67n/pLkSBpBt7alxOWOeV76dKlzRhh4x122EFSOy2JE51JA3niiSdmvYexQaqBp60QHueavCEEv/McP30YfUjx8KJN5itpX9iu/z9jw0PrgA0yX7ExqaRmkW7pp4bT9pKGHl5YuOWWW0qarnRUqX6KPOkx7BWeavDMM89IKnN6n332acaw4+XLl0tqr6XM4VqB8pjw908KoKdMYWvMTW8r3S3w972VAmFOc661SR3rHHVq7Y/9WmlRjI7ejKTbMIKUIqmsaaTuemob69zY0wJr2vl+y/6Khp4uTqoa8921I8WNFGi3U1Kox9pu26mtPaSzSeU7Hfr5WFc/n7uk3pPS66msfP+eRFplIiIhhBBCCCGE3plYRASPyS233NI8hpePu2P3wONZ5Q7OoyV4FvDuc7cnlQIcvFxebDbWg764BqIgUil8wztDsaEkPfjgg60x/7tddtlFUvE0eIs49MQD5mNj0849DNiEe1Pw0OAVvOOOO5oxvA94dPBCSMVmaSftRe54/NFxrB5WnzO0/XOvE60qmbd4VqRic3j43HPDAWroWTtEkuL2sRb6+5zxQn3Ai0ejDY8Q8xj6+2tRkI43m6ibVKJJPH+M2nWbaEh1/ShIx678cD2813gGsVNJOuSQQ1p/55FM5vAY1rjamsJjbi9Ex/yaiBihk89N5jf6u+fU1zh/banY11i80r62Afr5WO3AWqIXtZbm3RbHHumkaQLZB+6BZk8asn41fbpjtWicR3m6B7K6BmiMXXmkjr2UtcAj5WQ1DL1RQm1+vtJz/HmuX3dfnUs/j9R1D/0m20OSnnrqKUmTsb1EREIIIYQQQgi9M7GICHfTnjdNe8ptt91WUrlDk8rdLp6ZZ599thnDm00OtbfKo86hdvc+JvwOGW+At/PEI0p+OJ5CqXgfuIv2PGo8CuRhetvf2gF1Y6V2mJJ7kYlo4CV0u8SjgMew5jk85phjJLXtrHuY31gjIu7xRDNqYqQS+aH9p3sNiSKhE/pKpSVoN2opFe3cIz4mmDPuPUYz5qhUPFl4692z1T3UzL3SeF+7B4NJRTM+h7FEQaQyR7A5P3yVSE+tPomfrh/XzfPdM4itsVf4nGZ9HLJndS7PM9p521P2QV/7mZtcu3tCu4c5+nrI54CGvhcTbR+qR7/rzffr4jHmjx+Wh6auH3OQiJKve+jGZ+KtydljmbNul9SKDVG/7iGzfrBv9zm+bqOBRzbQjOusRR95Df9Owu98jr6fkLkw1Pqa7gGWbl+sNXOt1V5Hg25cq+uHNvw/Bx10UDPGHsT/51lHzONJRILH/e08hBBCCCGEMEpyIxJCCCGEEELonYm37/XwN6Fkis09hYGwFCEpD0nzGoRNPQxK+JSw2BgKENcF10tKjFSKztGHk12lEoKvhfGA8Lxr1y1iHLN2njJFkaAXR9Pak2I4P9Ebvbl+D/2SekOYmrQFqaS9EZIem37MMU+ZoojcW6GSPkOKgTdK6KYHeeiZMDE259qRakPx5pjSi6SSvkDKn1TaKHq6FoX6XkjdpVa4fcABB0gq6W3+msx3D+WPBdZprsvtDNvzAlUacaCj7wvdNBFaV0qlWJ30CD+hmRRDLz4eCt2ULF/XSMUgfcXXcjTwtu6kNM+V5kX679FHH92MkcZba5pCU5mhpsd0bcLb1bOObb/99pLa301oHFErLAfXrzv/P/rRjzZjtDDn7/1E9m4b/iGBZqQz+prD2o29uE3wvQwNpdl7odsxKWv77befJOl3f/d3mzGOEiD13E8U99TVIYKt0eDGSwj4rst3Lj9mAv1co659ePo8n8txxx0nSfrYxz7WjPEdm+83V1xxRTPmKYJ9k4hICCGEEEIIoXcm3r7XDwjC29AttpSKt4G7Sr8DxJNxwgknSGoXAVHwPvS75XXhd8D87m2KKUZEJy8koyCPx1w7XuOUU06R1PZ+U7yE52yIXpr1xd87HgMOg5NKgwSeh55SsTl09IP38G4de+yxktqF3XhtKKIbq37u6eN6Kc73cezFmyjQIAGb86gHXmkiBe5Fpa033u+xacf79YJh7M0987RP5LBLb/OJrkTpVqxY0YzhLWTtc0/3RRddJKl9gNhYwOvHWsV1SsVOakXnRDR8bWO+4oH88Ic/3IzxGfA5XX311c3YmjVrWmNDgvdUi4iwZrGW+aG/7Kne9p7DgWkZ7fst0fYzzjhDkvTBD36wGWP95O8+//nPN2NeuD5k0MPnJ2sbrfC9mQnNctxTTeSMNcoL37HVP/zDP5RU1jqp7BHXXXedJOkLX/hCMzaGKCaa+f5JK3Zs0Js/0M7d27p3W757s40TTzxRkvTHf/zHkkokVCpz/5JLLpEkXXnllc3YkJtLSGWucj1855JKS2f2QG82tGrVKkntwnIaEBG19e8rRN9+67d+a9YY7eL//d//XVJ7PZhkxkYiIiGEEEIIIYTemXhEBE+gVO52+ek5iOSlcqftnoxzzz1XknTggQdKakcDyFf3HOCxQ67ljTfe2DzG3S9emYMPPrgZI1+TnFT3WJ999tmSpKVLl0pq56HTGs9bFo4Vv9vHu8VPqeRAY3PuAcSjgLfVa0uOP/54ScUL6Xmwt99+u6T2IWtjgjnq18S1uCeWttl47j2qdvfdd0sqHuhDDz20GWNOo7kfrkTu7xg8hDWwN193iKx57Ru1XUQ2/IBXvGNEBXxO8xpEPTzPnDVvqHn6c4FuzDXPo6b2wW0P/agl8cg3+eTYnEfj8CQSFbjggguasTHVJdVqYvC4ettOdOTwXx+///77JbU9+kceeaSkUovk+wJe1c9+9rOS2vV0Y7E5Plv/rsAcXL58uaTi5ZeKvaCVVCJC7CM+r4kkEY3zzI9rrrlGkvQ3f/M3ktr1YUO2uW4tkbfWJorUPVxaKnPS1yhqu1gffU899dRTJZU91WvCiL79y7/8i6TZdTpDBt2YZx5x4/rR1Oc1erBWSUU/aoLdVg8//HBJxR6J3EnSX//1X0sq++tQokiJiIQQQgghhBB6JzciIYQQQgghhN6ZWGoWeOrCn/zJn0iSfv3Xf11SOxWBAhzSODwsSPiTsL6H6yj6IYw6DRD+9kKtP//zP5ck/dEf/ZGkdiEZYVPCvl5Q3S2S8mJZUosIHQ85bLwu/L2TcvYf//EfzWO0CyVdwQvJKMwmXcRTGEi3IXzsrWsvv/xySSXVcKz6eVrBN7/5TUntUDppH4SaP/7xjzdjXDOpHZ5Ww3ylAPRLX/pSM0aK0lhSPboQMvci8quuukpSu1idomBszNukMk+xN08vZI374he/KKldMEwr2zHaW3cd8gJXUm9Z76XSApoUIr9mmkrwWfjahn2RquDzdoinWneptcMmbZL12lNqd955Z0kl3UUq6bisa17oT/EwaZn+OXzmM5+RJH3lK1+R1G4eMHSbYw7xGdeKqg877DBJ9dPksTeprIvdE8elkvLFZ/D1r3+9Gfu7v/s7ScNvddyFNB7WFz/VnPQ80gI97YjvGKRKSiXlls+j1lwHff7+7/++GWOPGFP6JGBrrDU0UnJIm0czqXx/c9vjezM26LbKnGV+fvrTn27GSOkams0lIhJCCCGEEELonSUbcke5ZMmSXm4/a4fDvf/975dUPP1ezMPdIwXstHaTpP/+7/+WVArsNkZxzszMzJJ1P6vNYmuHZ56izdNPP70ZO+200yQVnfBoSMUzQTEeHlap6EhLxo3kKVw9MzOzbN1Pa7MY+rkXAW/NRz7yEUmlWFMqBbF49b24kIgbBWFe9MpjeEI2hvdm0raHBn6YJrb2oQ99SFLb68qcxHY8MklbQq1gRh0AACAASURBVCJHXsyI93oje2422PYWqp17mfESHnHEEc1jFCLS3tMPWMPTiha0lZWkCy+8UJJ0/fXXS2q3TWWN28jewl7mLes63mWPfJ988smSynomFU8/e4XbC15TIru0NZakSy+9VFJpre37wiJ5WRfF9nwN43cibhRdS9KZZ54pqUSVpLLPEjny7AR0wc6+9rWvNWPspTRK6MErvdFtj7nlRdVkW9DcgOMApBIpd69094BgjxDg9f7c5z4nqbTqlUqUpK92qRt7z2Bueltd1nzWM89m4Xnu5ed7R3eOSmU/+MY3viGpHTmYQHH1RrM91jbmm2tEu2eOAaABjFQyDrxhBDbEPP3qV7/ajLHOEVHyNu59R5DW1/YSEQkhhBBCCCH0Tm5EQgghhBBCCL0zyNQs+/+a3wk7e0EYdE8V9xAzhTsbM8Vj0ukx64PrxLkOhAS9QI/wPEVPXnhIutYYUzzmCyH7zTbbrHmMwk3SbPxsC7RBPw8dL0bofYi21y3W9HQHxtDCzyRBK9K2ekhV6D01q4ana/E789DD71193La6aSE9MJF5680N0KqWvobtefooKQk85nvABIpcF932uqet14qn/QTrbqqtr2usZ2g24aLgRbO9ub5juFbsobWi4NreuUjpkfNisfYMn5t8t0Cnmnbe9OSll16SVDT07ySTPOG7wqLZntsS6VcUq7t+7AnefIL0evTju9rQSGpWCCGEEEIIYbAMOiIyVIbolR4Rg46IDJ3Y3oIYRERkpGTeLozY3vyJ7S2A7BkLIra3ABIRCSGEEEIIIQyW3IiEEEIIIYQQeic3IiGEEEIIIYTeyY1ICCGEEEIIoXdyIxJCCCGEEELondyIhBBCCCGEEHrnNet+SovnJD22GG9kRGw/z7+Ldi8T/eZPtFsY89Ev2r1MbG9hxPbmT2xv/kS7hRH95s96a7dB54iEEEIIIYQQwsYgqVkhhBBCCCGE3smNSAghhBBCCKF3ciMSQgghhBBC6J3ciIQQQgghhBB6JzciIYQQQgghhN7JjUgIIYQQQgihd3IjEkIIIYQQQuid3IiEEEIIIYQQeic3IiGEEEIIIYTeyY1ICCGEEEIIoXdyIxJCCCGEEELonddsyJOXLFkys1hvZEzMzMws2dC/iXYNz83MzGyxoX8U/V4mtrcgNtj2ol1D5u3CiO3Nn9jeAsiesSBiewtgfW0vEZHQN49N+g2ETZbY3vyJdgsj+s2faBcmRWyvB3IjEkIIIYQQQuid3IiEEEIIIYQQeic3IiGEEEIIIYTeyY1ICCGEEEIIoXdyIxJCCCGEEELondyIhBBCCCGEEHonNyIhhBBCCCGE3tmgAw03Jq9+9aslSa997Wubx17/+te3HnvjG9/YjP34xz+WJP3gBz+QJP3iF79oxn75y19KkmZmNo0zZNCOn5L0qle9qvXYa15TPtqf//znkqSf/exnkqRf/epXzZj/vqmAVkuWlLN2+J2fPEcq9rWp2VmNrk5S0cMf645typp12VDtQgghhGklEZEQQgghhBBC7+RGJIQQQgghhNA7Gz01q5buQqrVG97whmbsve99ryRpxx13bB7beeedJUnHHHOMJOnd7353M/amN71JknT77bdLkq644opmbOXKlZKkZ555RpL0k5/8pBkbU3qDpwOhHelqrt2WW24pSdpqq62ax7bffntJ0uGHHy5J2nbbbZsxUtxWrVolSbr22mubsdtuu02S9OKLL0oq6VvSuLST2qlqgN287nWvax7bfPPNJUlvectbmsfQ9IADDpAkbbPNNs0Ynwv63XPPPc3Yo48+Kkn60Y9+JKmdMjgm/TyVD9DO0yfR0fXEvnbYYQdJ0pvf/OZmjLTABx98UJL03HPPNWMvvfRS6zmeJjh27dCgNqdrj73jHe+QVNL/pKLL9773PUnST3/602asm2Y55hTLmn6elgtco+8x6MX66Prxe82++H3sqYO1NY+56ddUsw8e4zVq86/2d2PXzPG5CKx3fn1uV9BNq/Tnz6XNNOgm1dNJmcvrsr310WBadFofunvDumxp2rRJRCSEEEIIIYTQO4tarM5dHnfJ7oE+4ogjJEkrVqxoHttpp50kSe973/sktb1ieJoPOuggSW2v6w9/+ENJJTLinsOx3jnipUKDrbfeuhk788wzJZXoh1Q0IxJCJEUq3lPGdtttt2bsC1/4gqQSJXGPdc0LNERqBdR4SIl+oI8knXTSSZKk5cuXN48RUdpuu+0ktaMA2NMpp5wiqR1ROv/88yVJt9xyi6S2fh4dGSp4YPx6N9tsM0nS29/+dkkl0iFJhx56qCRp6dKlzWNELnfZZZdZr0V08t5775UkXX311c3YZZddJkm6//77JbW1w2aHPH+Zo76uYW81uyP6W4tWMuYeWjz5DzzwgCTpjjvuaMbWrl0rSXryySclSc8//3wzhr0OPUqCfltssUXzGLphgx71fetb3yqpbV/YBxFNj9QxhkZPPfVUM0b0nGjTd7/73WYM/Ya8/qGd2xL6oIHvkcBeKZX16W1ve1vr7/01sCuil5L0wgsvSCo6uXbM2yFrJ5V59p73vKd5rKuX2xJzyXXgMWwV25XKvOb5RMwl6fvf/76kohENeKTZc3eI6x/7rM9NtOOaPMqJnbl2wPcUNJRKJB7N/Psc9st78OwXfh+ydlJ57+yvUrEXtPJIJ5piN1K5tlq2EfoxF/17CE2fsH8fQ7+5oqGLRSIiIYQQQgghhN7Z6BGRWm4gd3R+h4V3avXq1c1j3Pni7fO7Nf6WO+h3vetdzdjuu+8uSbr11lslSd/5znc2xqX0Ri3HtHs36l5APAVPPPHErPGHH35YUrvWo+udcg8uEab77rtPUtuzOnRqOfeAfnhqvN7one98Z+vvpaLXnXfeKantwQI8BkTupBIlwZvv3sEht/vF44LXzz1Y2BLe/GXLljVjBx54oKR2lARvdM37x3zlc/jQhz7UjO26666SpPPOO0+SdPnllzdjfH5EBYYE3ic8Wu5J5jHm1R577NGMcb3Ug/jz0QyPlVQiBUTrfuM3fqMZI4p08cUXS5I+//nPN2N4+/EeDiEy4nMNzzHX5fVYaLnffvtJamuFB9a9hbxWrUYL+0VT9+A+/fTTkkqN3Oc+97lm7KGHHpJU5rSvn5OYy64dHvz9999fUju6jS5EJt1bjK5uX906Jveq8hro6R5rIiJE6r7yla80Y3fffbck6bHHHpPU3ocmZYeuH/vAYYcdJqlcp1S80tgJ0TIfc03Ri+8ieKKlEuHEdtxmiYBQX3jllVc2Y2vWrJFU6hBrEaw+ce3YN8kk8LWNMezMv0ewB/g8Qg/s2aNR7C3dGi+p2C/zl/1aKt8bb775ZknDi7DzvYv9dM8992zGWAOxKX/vzE+fP2hDXbV/p+P7CdfqETe0/Pa3vy2p7BVSsUcyPpjD0uLvJYmIhBBCCCGEEHonNyIhhBBCCCGE3lnUYnVCQ4SDPERE+NtDQzfccIOkEvr1NBdCoxTnfeADH2jGCIl6aHlM1MKFhN5IOXAtSL+iCFMqITP0/Na3vtWMoQupEKeffnozRhoOIdVaS76h0i2q8vfebX3qoU60IbwrlZDv448/LqloLBX9aDlNsbtDON9TnLzIbmh00ybdBgljY3u1NDXaaEtFu0ceeUSSdNdddzVjpHQQLj7uuOOaMdKS9tprL0ntNE3Wilra4qQhRYLP1z9nUt1qaxI24nOT8DfrIekFUmlQQUtpb1iBZtg5KR1SWT/5XIaQmuV0Uy58zpCiQMqBp2yQRsMclYqdMF9JL5BKahZpTJ6aRVMKfnoazhe/+MXWa7v9T8IOa+2e0Yy0SKlcLykznkbEXPM9AzsmldnXQ1I9SHv21FY+B8YodpdKiiCpHKSASJOzQ9eP+cn1YWdSSQniMU+F4u9ocy+V68E+vJiY/5PvK546w9i+++4rqZ2ic+GFF7ae4w0q0LRPG/Q9lbWcdCrSJ6WSIujHMXTxtEC0rRXsk+ZFypunpQN71FFHHdU8Rnt9Ulq/9rWvNWPs/33PX7c90khJ0aXxi1RsgO9orjtauT3ye63onM8Jm6ul/NdampMOyN7y//7f/2vGWF+TmhVCCCGEEEKYGhY1IgJd775UCq29RR4enNrdGs/zlmeAd8gL48YOd7Fo4gX4eD/9bhtvTK1os3sInUdXuHsemtd0Q6gdsMXvFM1RWCnVC6HxUvP8mpebYmxvyYp3qBaVGTLog534+8Y7hWa1VoJuj0RC8O77PEc7PCreVhrPKq/pa8EQIyHQnZu1phr8dE8fvxP9kErROd4ofz5RFVpDe/tLtMOT78XEtQPZJo2vVd1WqH5AKPOO9+4Fsaxb/nwiSLXiXqK8eLjPPvvsZgz9iFh5VKbbinTSOrpHGB3x8N50003NGO+XOeYRINpn05REKlFN5rmvhzQJYH07+uijmzG8tniefd/l72pr8qTmtK85fLbYErpIRS/msx8fwP7gETciuGjqHn8iVXjBafLhY+wjrh/a1g62m4R+rh2RCubYs88+24zRzh193WbZK3zPoKAc7fw7Cfqw7/gawJwmAuP7FhEt/q6mXd+4DthXra0u18z3W18v0cbXNiJl3cYaUokqs5Z6MxDWOTT1ltPYIw0AaoeTLxaJiIQQQgghhBB6JzciIYQQQgghhN7pJTULvI80YcxaOhUhNU8JIWRFiNiLogjzkZ406VD6xoRr8VQh0odq6Q417ShaIhznhYcUL5LiMeYULQcdsDlPayEVwcONpNnw/FqBI0WZnkJEuLl7Wu5YqKWUETLmmjwdAdtzOyF03NVQKp8DYWU/3Zr/h7DyEM8MqcG1c71uD9gZIXNP1yCVwx8j5aOW5oWO2OtFF13UjJHShU3WimiHtA66vbCWYUt+DgspfBQ5cy6FVNYoT1+jwLpb/OrP5//x1BAKhSm49qYp2OFQ9HOb4HMm/dG1QGNSrtwuH3zwQUntFBgvTve/l8r+wdlcFAJLpUj58MMPl9ReK3mvtXMbJqWnryukEzEHvaCfNJXaWVKkR3qjCVJSa2lovv9K0pe+9KXmd4qVTz75ZEntM5mYG/zf/plMQj/XDnupnY1CShl7hc+1lStXSmqnBTKvu6msUkkf4jG3YxrGnHXWWZKkFStWNGOkhdGsw9/7pGzP5y62Q8qfN5qgWJ357Wlvl156qaR2GibnH/H6fn3oV/ucSK8788wzJbXTVWmswGfnqWCLrV8iIiGEEEIIIYTe6TUiUmsj5kWCFGdyB+ynp3/iE5+QVFrPuifoggsumPXYtOEeA+6C/SRXfufu14tXTzvtNEnSBz/4QUltTwHRJI8YTBPYnEfe8IK5J5aoEdq6FwFvxbHHHiupXcSIl9vbf44dbA0t3NtO5MTti3mKrq41Hhj3+gGeavfSAp/bEIvWu+/FvfDdYmuackglqutrHrbEdboHjUJR1kNvvUoRJF4rfw+1BgRDotssotbWk+vCuykVPdy+3Bvf/TefAa/vJwWjKcXqrhWfYfe1J0WttTY/XR8e4327tx+t3cuJzfCzFhXls3LN0QwPKh5+fx7PGcK89b0TW+BnreU/z3evNFFbb9GLvrWocre1vEdXmLvMZz/dnc+C9XXSEXbXjmsg6uGRCnTEbrw5TC0ah3Zz2Ud3D5DKvox2tZb9REaGEBFx/fh+SmMNL2Rnb+A9871MKt8x/LT6+Z4Uz/OJbrl+vCb6+fqx2Jkyw1hpQwghhBBCCJsUvUZEanh7OFr/4WH92Mc+1oxxgCH1DZ6zijd/0t6DxaSWa+veHHIn8eyfeOKJzdgZZ5whqXheLrvssmYMDxl3w0P1os6Xbq2IVLw2rh8ee7yJe++9dzN28MEHtx679tprmzE8BWOPiNTsq3vwnFS8JB6tZE6iK/NYKlEnDjT0XHy0w9vo0YDa+xoa3RbI/nvNg4R23oKclo544T1awpxkTvvcxJvWPbzQ39eQtKvZF3PNvcVcI/uC7w/8Xa3Vbs0DjyZ4UT3CwfNZ/3ysZveTxNeubiZBzWvPtXXrFPzv/TW6OfnS3K3wu1q75xTNhxRhd/34vLtRCank8JNd4HWZ/O661Dz2MJcHGZvuHowqFa83tVKTrrHx62CPQztqZKRS00WE16PoRJFcu7n06V6n60sUBtv21+E9UNc0hO+Dfi1d/YgUSUUvrs8PbWWsZnvrQ00/9mVfS7E9alLcLlMjEkIIIYQQQpg6ciMSQgghhBBC6J2JpWbVUmYIG5EK46eRUvRKmImQvFQKu/h7DxXX0j3GiIfGukWGUgn3LVu2TFI7tYjTcQlneupMVzsvoBrKycILoWZntZNNuX4K072AkLQitEJPqaTZEG73dC9Cm0MIEc8H3reHaEltcJtgLm699datf0ulJSYpSF7oz2txurEXwNfSSoZKbX6Q1lNLheFEaqnYD3bj6UCkNBBG91NwSSWkHTLro9QuIh0ytSJfYA13PXi+t71EN1KrvGEJf4se/nects68pQ1y7TWHSK1tJ3Dd/v5Z131udgu1PUUOmIdue7Q8Jj0TLaVy4jP/31BTfWtpUewLtTWO63BNPa1Fqrct5+983ePoge22205S2V+k0uK2drL6UOA6vfFBt3W77621duJdu5jrOn09QzsaJHgTlKuuukpSfe0dko61IwWwQ3T0VNuFfn9wO+X0dL7nkNIvlZbApPvONxVsPgx3pQ0hhBBCCCFMLROPiPhdFx4Z7hS9VR53udzduSeDYlkOWlq7dm0zxuEsQ7ojXijc6btHgkJFWlR6EWO3MKx2h0wkZc2aNc0YrUfH6tF3/PPHlrygjmvFE+VFsocccoikUjSMd18q3kG8CH7oEIc2jV2/2mF0fmBVt5WsF7LjLSVC515XfsfresMNNzRjePr5/8Z20GYtEoe3y9tYcl0eDQLsbOnSpZLakUzmLWskBxxKpd0j83yuwuMhUFub0a1WQO0Rb+ZrreCaSBKHrXmbWaLu3WYBUjk4Ek/skPWba21xrzO/uz4e/e4+v1vUigdVKpkK7BkeTaodQjdEsLlaQ4JaETp25pHabtS2diAkUSfmq1Rs78gjj5TUPmCY7z5DjSRJ5Tp9znTHnFo0icdqdtltyLDNNts0Y8uXL5ckHXXUUZLaUc5uUXctAjOE74Fcn0c9+H2uLJTafIba89Hb92N04zBSj3TSrAEb7FO/RERCCCGEEEIIvTPx9r3ukcCrf+ONN0pqHzjHXS5eHI964HXgwDm/A7z44osltaMrY6d7UJdUPMg85rpyl00OP23upHJHfPzxx0tqa/5///d/koq3fwjehI1BLUeTVnp4BfzwIJ6PR9Vzq/HIoJ+PoRstIsfm1a/BPPSIG3OLloPupSJXmOcTQZJKtASvtHtoH3roIUnSXXfd1fp/x0atnay3MMZGugfsScVeyOvHiyWVvHLqTTyCh52yng7hYK/54u+XSIjPTVpidvP1pbKWMX89HxqNdtttN0nS7bff3owRQaF2qXZQ3aTYkP+/dhBiLa8f732tZo4osNcc7rHHHpLKXGauSqXmCW9szYs7SQ27//f6HKjn1Gof0M+vlflIFJOouiQdeuihkqS99tpLUrtVay2yB5PWb66DXOfLXLU3aHfcccc1YxxLgD2yX0vFtueqn5ok3fezvu3Bu9Gj2piDlqx/J598cjPGgeCse34gKvtS96DOPkhEJIQQQgghhNA7uREJIYQQQggh9M7EU7M8XEVojRM7zzvvvGbsgQcekFTaM3pYi/SE3/zN35RUUpAk6Z577pFUwuzT0s5XaofO0IUCspUrVzZj3VM7PY2DQsNzzjlHUru9I9rVTkadBtwWuEbSZzyFAU1JE6LtolRO4SXk6ekz2BypJP6aY8cLhrELis+9IL12ejaQroV2FL9K0i233CKppC14Os7QQu7rC+/btUBH0hE8NYvfSY/x5go0QkA7bwFKS0vsdWO2gpwktSJP5ibpMV4k3W0l6uslaZmsd57OSytkGgh4Wu9Y0ytrBcakdaCdp0byOxp4owTsEe28NW232YcXdI/Z9pi7c7WEddtDEwrRvdifFEGe76/J2lArjh+zfl3msj20IwWQAmuprHOsl76WdlPIp007v55ukw5PTUU/mkp84AMfaMbQD/09NYuGPbX234utXyIiIYQQQgghhN6ZeETE6bbU8zaXV155paRyJ+d3gHgd8BK692GfffaRJD366KOS2gdeTRNoh/fPr5O2qGjmERG8p2hHAZ0kHXDAAZJKNGraIiJOt92qe/yJDBGpu/POO5sxiuYo/vKDEClQpBDWvTdj9erXwKNX86Qw7yicxusilfaV2JwfFLlixQpJ0tVXXy2p7bkZu3b+/tGudhBYt9i8VkxMkwqPAhNZuvzyyyUNuxXofKh5pWttQIlA0mraC9KxNRp3eJMODk3DSzt2e3Ncn+5eWivK7zabkMq+zE/fiz2yJE2HJ1qqR0S6Xmn36ncPyvS/Q1Miex6lIspUO7R47NTawaITmQVSibjRzMSzNID9mTVSKp8H31OmSbsuREfQxtu/0zCCVsd+gC42in7sz9Ls9uh96peISAghhBBCCKF3BhURgVqbuq6HwL03eLxowepe6W233XbW86eZmnZ4VGv1D3DfffdJKq0rpZIrXWvFOE1ewnWBpwD9aBkqFW05sMpzqfE+1No7TpN+c7UXxLtS0w4vId7pww47bNZr4jHzKMtY8/TnouZxRTvszyOSzE208zWP18JT69pNK+hWOwCR+iL2B6kc/Ep0Ey+sVDzU6Djt+uGhr+WEE4lEL6lEdomiu11Sx4SG07bm+TXwe+1aqe/CY+8HwKI3+vkYkSfm/jTpV4sEY0s+Ro0DWnh9IFozb70VOvWEXkNW+7/HSq2+mf3Ua6bRD7vy7BjsiuwOIupSyXxBv5qtLxbTvcKGEEIIIYQQBkluREIIIYQQQgi9M6jUrG7xlxfL8HstRES4jpQjDxUTuvLWZ9NIVztPUUA7HnMNCY0SxvNWlfyOdtMQ3nwl0I90Ktevm/ZRawNKEbYXz2GPkz4Rd7HpFr2ur3acikvKjLegJZyMZpuydrUUIVpDk1K5+eabN2MUEU+7dtLsk5ldP9IVWP+Yq5K0evVqSaW42k9df/rppyWVfWSaUgHnsgXfb9GOvdW1o/nJvvvuO+s1SeEiBWTabK+WXoQNun7duevpRTRNYM7SMEEq+wi6T4Pt1dahbovd2injPMf1Yd1jbj700EPNGGlG06SdVNevu7bVTqbnMdePMdLeaMTjj/Hafc7dRERCCCGEEEIIvTPxiIgXY3F3XPPcd5/vf9c9YMk9E9PsjZ5Lu7laXPrdM4VNPFYrxK41CJg2Pbse1Zr3pmZ7tFGttbmk0HOa2x5L9dap0LUd1w7N8eBTQOe/T2u7beh6U2tzrObtx94efvhhSe3D1NauXSupzOVpm6s1ut5pqWhai7BTkMmhj+golSJiIsLTrl/N44p9oZ17rFnXOHTU2+zjYa0VvE7rXlzTD1vzwx7hySeflFSimRRZS8XDz54xrfrNFa1lLtOQw9c25ilF6u7RR1dsd1q1k9Yv2k2jJj8kl+wD9gg/ioCo3SQiSYmIhBBCCCGEEHpn4hGRGjXvKR5/fnqb1COOOEKS9P73v19S2/PFnbMfUDeNdL2nNc8z2vnhXfvtt58k6eSTT5bU9uB0WwlOG+vb0hn9+On67b777pKKDW655ZbN2K233iqpfWDVtDCXdu6lwebwrLp2HAK5dOlSSe0DDbvaTYsnS6pHPWp6dg9K46AqqRxEWmsb3W3pOE3avRI1/fCksqa5fl1vq3tdaUE7V2R+mqjttxwwx4F8XoOEVtiZe1C7UaQ+W4BOim59plTmI4ct+yHCfD/Bzrydfq2d7TTSzc7w7x0cIbDddtu1niuVaAdROa+9QceadtOmJ5qwvzJPpXKIN99F/CBlfqeWFRuU6lG4vkhEJIQQQgghhNA7uREJIYQQQggh9M6gUrMI01F8vtlmmzVjW221lSRp7733liTtv//+zdjOO+/cer63UKUd3rRDqI6Quoc6SUmgPSptF6WiJ20sPQ2LsOe0nyzsUNjlBV6kExEq3nPPPZuxZcuWSSr26bqTVuQh+2mGFCLXjrlM4RypbJJ00EEHSSrpRVtssUUzhs2RBrK+aXRjoFZEiY3MZXfe3pjw+4477iipnZrVXQO8mHhaIUXB9SP9iv3BU/+23nprSWXd8zWOfYR0Gk9tmLaiV6meHoMu7A/bbLNNM4bWzG1PLeIxUj68Ucc0aicV/fz7Cu2gWe+Yk1JphUxzHf87nkeTDk8znybdsAX2DF+/dtllF0kl/dQbJXhTCamd8sbvNEqoFV1Pi4boxzWTAihJe+yxh6Qyhz19jcYIfM/z7yZ8FnM111ks/Tadb5ghhBBCCCGEwTAoVy2eFgp/jznmmGbsuOOOk1Tu8vxODu/BzTffLEk6//zzmzHuoGsH5kwT3M0eeuihkqQjjzyyGTvttNMkFa80z5WKN4s2bldccUUz9uijj0qazoLhLmhCtAgdJemcc86RVPduYXt4Gq688spmjGgcHppphbmI5/7AAw9sxj784Q9LkpYvXy6p7flCO4peV61a1Yxx2OFcBYhjpdaEA+89kQ6p2B1zGS++VGyKnxwyJxWPqx9OOq10o+i77rprM3bmmWdKKtFzimClog3RohtvvLEZw+ZY92rRuGloZY52rGcecTvllFMklYYSeKmlEu1gjl533XXNGJ5WvNEeaaq1WB6rdlKZu0Q00EqSTj31VEnFq+8eaw6YY3/lAE1ptjfa9as1thjToX21dY8ia1/3jjrqKEkly8ALsbHVO+64Q1I54FCaPV/XZXtj0k5qv3e+r6CR77lEMdHYI8EcXE3WkDfSwfbmyj5YLP0SEQkhhBBCCCH0Tm5Emdoh5QAAIABJREFUQgghhBBCCL0z8dQsD80S6iHNxYtsOHGUMKYXod92222SpGuuuUZSObFUKqGnsYXhNhTCkBS3ec98Qm5PPfWUpHYx/+233y5JuuqqqyRJ1157bTNGige9u6cZQp2Egb2gn3Qi0g5cP05mJj3hG9/4RjOGHU/jeQ610+VJPzj66KObMcLEpC/4SemkJpBW9NWvfrUZwy6nMS3QtaPYkIL9k046qRkjJYtia9eA1CJ0+uY3v9mMcTI4a8G0rX2ecoF+FO0fe+yxzdjxxx/fGqulNpC663sGqUf8P+uyvTEVYddsj5TKgw8+uBk78cQTJRW79HRezhRhHfQ5DawJ3vykptOYtJPq6x4pz5yHJEkrVqyQVIr8SZORShMKzuny/ZU0Vz6bdZ1BNSb9araHdp6atddee0kq+27t2mpNiGhUwXrnTSbmej9j0E5q68e10hSBNU4qNkcam+vAXH/ggQcktddSXhOb69P2EhEJIYQQQggh9M7EIyIOd2B4+dasWdOMcQfN3a4XdXVPcR3LHe7GBD3wzHvxJZ4F9KU4WCreGLynm0L0A2ptiVevXi1JWrt2bfMYBbBEiLy4kGJh9PdWltN4MnOtEBAv3mOPPSZJ+td//ddmjIgRutx///3NGB5VPDYUv0qzT3mdBg27p+FKZV0jwnH55Zc3Y0Q1acLBT6l4tCi2vueee5oxXgv7m5aICDbn+tFylmsk6iuVwkxsh4YcUtljKBz2gmvssmZ7fIZj07SmHbaHZ99bH3dt9ZFHHmnGKBQmmuRrZVdzh9fyxjFjactdm7tEiWqtt7stfZ988slmjAJr1jsi51LZm9mPawXeY2u8U9sz+L37/UMqa39NO+Y3+wknrEtzt+1FuzF+v6npx/wiGulRD77nUcjuGRzdBiYesZwrc6ObcbOxSUQkhBBCCCGE0DtLNsTTuGTJkvG7JTcCMzMzG+zGiXYNq2dmZpZt6B9Fv5cZm+118+wnHNnYYNvrW7uaxxoPn3tC0bFHz/yg5y125gec4RFEN48E43XFQ+o6LpKNDs72uodpUvshlcNG8Zh6pIkoHF5Y96riMd3IGg7S9rqHrvqBrNQnoY1HPYjCUYvkXurFqCcc4p7RPUTTtTv99NMllaiH2x7ZCNTX+GGti1SLOUjbY5/gUExvD33WWWdJKvblEaVnnnmm9RhZHlJZCydhe4mIhBBCCCGEEHonNyIhhBBCCCGE3klq1jwYYqhzRAwy1DkWYnsLYnDpMSMi83ZhDN72aoXjA2kSMQrbG6p+Y9gz5tLOxyag51TZXt8ti/9/e2cWbFtVnf/vxr7BDhsUkR6Rvrlc6UVBwKARgmg0VSF5SJnEMsmLSSVlKnlKKn2qUumqjCZolVpYMXTSheaC9NwrSCc9VwEVsce+uf8H/r81vzXPZN9zzr1n7bWO3+/l7LPn7tbYY8611xjfGDPSrBBCCCGEEMJoWWr73sclbVqJDzIhdl7m82K7J4n9lk9st3Usx36x3ZPE97aO0fveGKL3T8EkfG+k9pu87eZs18nbbzmP20Ys2nZLkmaFEEIIIYQQwrYg0qwQQgghhBDC4ORCJIQQQgghhDA4uRAJIYQQQgghDE4uREIIIYQQQgiDkwuREEIIIYQQwuDkQiSEEEIIIYQwOLkQCSGEEEIIIQxOLkRCCCGEEEIIg5MLkRBCCCGEEMLg5EIkhBBCCCGEMDi5EAkhhBBCCCEMztOX8uA1a9ZsXqkPMiU2b968ZqnPie06Ht+8efPLlvqk2O9J4ntbxZJ9L7bryLzdOuJ7yye+txXknLFVxPe2gsX6XjIiYWg2zfsDhF9Y4nvLJ7bbOmK/5RPbhXkR3xuAXIiEEEIIIYQQBicXIiGEEEIIIYTByYVICCGEEEIIYXByIRJCCCGEEEIYnFyIhBBCCCGEEAZnSe17V+QDPL18hGc961m9+3760592Y5s3P9kN7Wc/+1nvb/24XyR+6ZfKdeQzn/lMSdLTnvY0ScVefhub/fznP+/GflFtJ0lr1pTOctjNbQrYz20Kv8j2A+xY/5UW2szHfA6HJ3H7zBrzORwWxyy/DCGEMB+SEQkhhBBCCCEMziAZESJRZDwkabfddpMkHXvssd19++67ryTp6KOPliQ9//nP78Z+8pOfSJIuueQSSdJZZ53VjX3+85+XtDqj09iOjIck7bDDDpKkI488srsPex5zzDGSpJe+9KXd2Pe//31JxXaf+cxnurE77rhDkvSjH/1om3/2MeGZN/xq77337u575StfKUk64ogjJEk77bRTN4Ztrr/+eknSTTfd1I3dfffdkoqNV2Ok2iPJ2PFFL3pRd98LX/hCSWX+4ov++EcffVRSsZckPfjgg5Kk73znO5LaGdDVBHYk+yaVNfFlL3tyzyzmtiRtv/32ved/9atf7W4//PDDkortfvjDH3ZjszJ4qwH3RzKYz372syUVX5SKj2633XaSpB/84Afd2De+8Q1JZd4+8cQT3Rh+uBrnslP743Oe85xuDJu94AUvkNTPFGOzH//4x5Kkb33rW90Y95HtXK0+6GDHZzzjGd192JJzjY8BNvr2t7/d3cc8xgdbyobViK+J2IrfPG47buOPPm85T7fm72q2ndRWd/B3lurIzxvcbilnVtp+yYiEEEIIIYQQBicXIiGEEEIIIYTBWVFpVp369RTR9773vd5jJOm1r32tJGnXXXeV1E+zkxp68YtfLEm67bbbujHkHqtJmoVdSEF66hKZGnIESTr44IMlSWvXrpVU7CSVFNsuu+wiSfrud7/bjX3pS1+SJD3++OOSVk8Ksy6cdmkB97m8CDngKaecIqlItfzxyN4++tGPdmOkM++55x5Jq1PO0ZLCuH8dfvjhkqTTTjtNUpnHUpEmMN8vvPDCbuzyyy+XJF133XWSpG9+85vd2GrxwxY+b5mTJ5xwgiTp9a9/fTf2mte8RlKRI7gk8Pbbb5ckXXvttZL6krfVLrP0ucwcxudaUt9Xv/rVkspaJ0lf+cpXJEmf+9znJElXXXVVN8ZayDq7mua0z2X8cMcdd5RUZKmSdOihh0qS9tlnH0lFjiWV8wdrns/pBx54oPeY1Sy3ZF4ioeQcLJV5fNBBB/UeKxV/wgcvvfTSbmzjxo2SpC9/+cuS+tIZnrca7Mgc5jfeXnvt1Y0dcsghkspvGZeoYkf8kfVPKueR++67T1L/d85qm8vM4+c973mSynlEkvbbbz9JZQ67zBypJVDWIEnr16+XJN11112SpK997WvdGOcUbzCzLf0wGZEQQgghhBDC4KxoRqRuG+vFgtwmAiCVK+HXve51ksrVnkNhnF/ZrparXKe2nUc5iZLceeed3X3r1q2TVCIFnk2qi+K88H21UhfsemSO2xT8SiVi0moOwBi2fclLXtKNkeUjY8VjVxOtgkmPkFLYShbp5S9/eTeG71FU+KpXvaobI1vCPPeizdU4p1vtjbEHkXwi0NLC6B8ZEqlEq/bYYw9J/UL2r3/965JWpy9K/YwIPvfud79bUokCSiVrR4GmRwMZo6mH+/g111wjqdjU7Tj1aLTb7hWveIUk6dd//dclSUcddVQ3hl05j/i5m0jz7rvvLqk0WpCkc845R5J0yy23SOoXsq+Gdt0+d/Gd008/XZJ00kkndWM777yzpGK/Vst8MnbMYalkly6++GJJJeskld8AU/VBtx2ZzLe+9a2SpLe//e3dGPbAdi01A+dnbCiV7NNFF10kSbrsssu6MZpTrJbif865+Nw73/nObozsMDZuNQLgL9kTqSgbrrzySknSJz7xiW5s06ZNklaugD0ZkRBCCCGEEMLgDNK+t47uS6XtpEdBb775Zknlis6jKVwBcmXmY61N6FYbfiVKdMr1o1/84hclFW0pEQCpRJ7R72J7qUQWsOFqiFq18Kt3Ipxes8RxE312zT2tGO+//35J/VoGovnY0TNXU464OK3j8OgyLWfRnLsWHxuj233ssccWvAYZJiL50ups/0k0zzXPhx12mKSi8fX5hy+21jxsRpbE5ztZZub5arEha5Tb7+STT5ZUovleu8Q6iT/6GorvMW9/+Zd/uRsjYo12fzVklvA9z5RjO/5SSyMtzKJ7Rpnv4bnPfa6k4sP+PmSTXKe/Gmoc+B0ilbqud7zjHZKkPffcsxvjWGln7PbjPuznWWL8kHWylbWfaraY45WkN7/5zZKk3/qt35LUb6Vf/xbxNZFaQ2zgW0Lgh8x7ah38eZ7JnxreXps5+/73v19S336cc1u/u5nP2M19idps5jC/d6Qyjz3zvi3bxK/+X/AhhBBCCCGE0ZELkRBCCCGEEMLgDCLNakFKyGVCSDNoyebpJiQIpNa89SryGMamnPp9KlrH1Cpgp62nFwwj48DWnuIjVe8pt9WI24/UpRekcxs7eIEXjydd7pI4JB7Iknyn19XohxS5eYEq7QE5XrcBciLkRS4hwmYUxj7yyCPdGOn11WRDfMzbGx955JGSirzN5WlIir7whS9I6ss7sD+72LtsCxkcNkQKMnXwPYpSJeltb3ubpCKLcf9Cpor9vFiWglhkqy5RQCZHW1uXc0xVpsUa5udUJEUUrPq8pRlCy/fwVc67vlZSwE3TAD+v8N1MUf6L/bxNKg0SaNvr5wXmMXPRx5B3IY9xuRw+jszo3nvv7cZoNT21Rj34h9vuzDPPlFRs15JfPfTQQ5L68j5gHrpMk7nMmviWt7ylG8P3fC1s7V4/RrAfxyVJ733veyX110LgGB999FFJfSk5cxz/9N+JSAT5bU0TBqm0mvbf63xPkWaFEEIIIYQQJsncMyJ+Rc9VF5GsN7zhDd0YUVei+UQaJOnqq6+W1N+AZbXh0bzWFShXuFwF+wZBXPUSRWDDLqm0cHR7rkbcfuCbTBGxwvfcfhTP4XteXIgt2WCz9T5Tx4+J6IwXq5PZIOrkG/YRdSKq5ZkUIqoPPvigpH6Gigjk1KP5rQ3kPLJF1J256ZFB5jSRQfdJbEW03os2iRLyfUy5/azbD39hozOpFFjWbaKlEs2//vrrJfXtR/SU6LRH/Mkg89cj/rzPFCLRbjvWLt8wk6wQPuEtetnojFaevsErfkXk1CP6vCdrpGffWW9bthu7XzJ3vcWxZ5ekvkJhw4YNkkoLWS9yp2gb//L1gGYLnJfJMEnF3t7wA8bsj9iODYGlYjv8xTNGN954o6TSLMLtym3OP76JJNs+YCc/TzPmGdNWa/8xwhyiOYLUX8ukvv1oVkIbY37TSOX3Cj7IlhmSdPzxx0sqdnP7velNb5LUbwDAecXfe7kkIxJCCCGEEEIYnFyIhBBCCCGEEAZnbtIs0mK+BwgpvDe+8Y2S+uknUnGkximUkYrsgzTf2NO8y8GPiTSs7zzPbeRsvkMzdsV2XnBE2s7lB6uR1o6qFF1KRc6CdIHUuFRSkMiEkBJJZf8WpDFux9XQN7+GOeZyAuQDzFeXWCGBISXue4zge7ymrwW8BmnzMUsPFgvH5HMTeQZyApcLYlfS6MgupSLPwLdcbonNeT9PnU+tUNjlRZwDfDdlJC/4ju8NxHHTO9/3BsJ+yIr8fep9qVxe1CpwHev89mNC8uOyNo691WQCW9Fkwn0Pe7QkMKwL2MklXTRUcKngmOe1+wHnB5cXYVOOAWm5VBruIGXxecc5gue71IrdrbGfN7agcN3lRWOVrrrtOJci75GK7/H5fc+Kc845R1IpznfJIHZkjXPZFjJXfgv5vi74qEtY/XXHhtuP40I6JS08/jvvvLMb+9CHPiSpyCt9rz7mHnPY59+6desklXOQF7LTaID3ldoSweWSjEgIIYQQQghhcOZerO7ROq7+KUDyAi8iXfVuzFIpgCUz4le6Y41WbQ0ck0cDuJqlFaMXEHJ1jZ3cdlxtEw3zgn+iFavNhkSkvKiaNqpE9Dyqjx2IqngBIfcRqXHfIwLI9zTm6N9iIdrk2aQDDzxQUvErL/7j2Ov221KZ+0RgPMJSR6X9eVOyox8H0WEab0jFjtjAI5zch495JhM/4zHuy0Rvd999d0mlaFsqdpyKDT3DSHG17wBOhI5In0f1yRJ5q3fwKKHUX0vxY1rQevtLIrdTyCx5duiAAw6Q1I8SExXFJx544IFujOOsM5NSWdda53DOMZyH3HZkj13NMOZzi2cnOT9wXFKJrnOMN910UzdGhBo/8SxQHYm/5557utv8BqK42u1O0wXPHox1Hvt6dNJJJ0mS9t9//+4+bEsWicJ0qUTysZPbqz5eMnZSWR/JmPpO7mRlptL62NUuv/qrvyqpn0nnvPLwww9Lks4+++xujEwSa2HLfmRLPYvHOsB7+7kLW/r5aVuugcmIhBBCCCGEEAZnbhkR8KsurtbQo6KhlEpkjKs716hTF8FjiLxI5SrPI15Th6tZjzoQuSIq4GNEH4hc+cZCxx13nKRyxevRGbSotBEdcwRhS7if4SceKcVe2MiPlagUf8kiSeW7IDLrUUgisuiEPao/5khgjUel8TPPiNT+5cfJfMVOno3jtZiv3g4TH7355psllciPv88UcH/AR7yFMX5GNtdraLzuQ+pn6Xge0WVfK8m4kD32qLTXN00BP2bs5tlefI21irapUsnuEo12P+Y+6pQ8AkmmgMySRyLJGox5/jLX/JjIjvmah8/hE7SalUqklKinfw+17dyfyTSRxXPbfeYzn5E0/vMI9nNbkfX1eca5ltbtl1xySTeG/XgtXwc4jzC/vZaH76KV/avPQ2OE86zXF7A5o9uTrNoNN9wgqZ8RYRNM7NJqic95xTeApYaG7J8raqailsFPdt555+4+snG+7rHeXXHFFZKk//u//+vGZm1jgS3xIc+yYz/qZL1FP+/n9UnJiIQQQgghhBAmTS5EQgghhBBCCIMz9/a9XlxIyzvkG14YQ6qK1K+n5BhDMkOxk1TSpshjxp4WXgwcg++Yie1IjZKKl4okgZTdI4880o0hFznxxBMl9VsmI4chpe7f1RjTmrNotdtsFWeSwnU/wV78dekR9qa40CVL2P1///d/JZUiMmlaUkG3HQWaLpVChoBkw+U/SKvwPZcMchsfxIZSkRch1/rwhz/cjbn0cuy0dgb39DYF+viWp9gpTkVa5alypDLID7wAnhajfFe+jlLcOfZ1ELt5wTBSKZdXsAZSKOz2u+OOOyQV+ZpLlWp5q6+X+BzyQP8OL7744gWfYWywJnmxLjvQ+/eOBPr888+XVGQyUpnTyCC97SnznO/GZVtIcpBOu8197o8Z5DEuOaMA2iW++N6nP/1pSdLtt9/ejXEeYe65H3Mf5wcf4ztjrrvv8R2M+dyLL/jvCAr83XbIr2jV60Xn+Bx+5rI27uO1XFqEPetGJ86YZW1SkYXSxlkqDQz8uDhvnHfeeZL6ciyOEemUS1IZ47VacnFs7NIrHrdSsuhkREIIIYQQQgiDM/didY/WcZW3cePG3v9SuSrkys83VuEqj6gFhWVSieYTRSVSMWW4UvUWlGxAxdXv+vXruzGiKkSxveCOiBUtk9/61rd2Y2QJHnroIUkluu2fYSp4FIlopkemyaIRffENgogOUkjsRXe08iXqw2ac0sJNIn1DtalmRIhAeSQFW5F1vOqqq7oxooYUZvq8JepHNPqMM87oxijWJCp94YUXdmN8D2OODIJ/RtYeb1+K7VinPKJPQSfP88gp0VGyx95WlAJXMgi0TpaKT449MggeEWbO+LrHmvY///M/kkoWSSrznCieRxTrjIgXh1IUih19vk9p41ffiJFjd9uxvjNf77vvvm6MqCh/PZOCT7NW4mdSiejTLtUbJYydemNVb0rCcfncJQNy6623Suo3l6ij0v48bMn7+JrIHOcc7fbDn8e47nEsZER801F+Y/g5j3UPVYL/LsM+2M6fV7efbZ1H+a58jOzfWDPBrCv4gm88yjz238r4HufXVhE+fz2LUfuOt5XG3pxb3GdpAb9S541kREIIIYQQQgiDM/eMiF+hccWHXtAzImRJwKMI6KP56xGaY489VlKJlLlOf6xXx1ui1vJJJWpPHYdrJ2u9qkfKaLNIFBAtsVSyJLSPcw2sXy1PDa7q0alKJdtD7RFZIKlEpWpttFTsRjTBIxmHHHKIpGIr90ui3WOMbtW0skne5hlfY255ZJUx7OO1XUReyK4cf/zx3Rib/xHB9dobom9TyMq12kB7DQ2+dfnll0vq195g69Y6hQ14jNeLEcEnaujzfUoR/RqOwyPP+BwZTY/4ExGtI4TSwjaWzEepZDmxu0ciZ+nPx4bXiJA583Wb+kmi0p7lZK1r2Y7brQ0NqRHBTv5+zNexr3l8dq+7ol7EI8/MY+q7PAKPX2Gj1hyu7ejvSWbBMwVTUHPwuWmhK5Xznn9+lC389Uj7YmzXaulLFp1Mna+JY8yi+zFwG5XAQQcd1I2xnvsaxbxivfPjqu3XOk+22krXtTy+2SG//VbqnDudVTWEEEIIIYSwasiFSAghhBBCCGFw5i7NakGK2FuSkf4l9ecSA+QepJuQG0kl1bX//vtLKuloqZ9ynyKeJiPtiTTLJTDYDvu4bItUM1I3ZFjSwsIzb+HIdzSmVOdiqXezlkrhJnZz36v9xFPwSBhohuAyBdKeFG66nJDvYmr249hduobEDRsudvfVuumCp+Dr3aG9de2UpFmefuc4XbqGFAt5R0vK0qJOu3ubVMaQELqUcGr4Z0fG5tI2GiQg4/X5N0t6W88738Gadc/b0oLLYcdKqwiatc7tQwEqa56vcy1JVg2+7a2PWes4V/j7TUFaJJXvHTmbVOalF4/je6x3y7UfjXgkac8995RU7Ndq9T2Wc0ZLWoSMyBvicM7wY6lt5/OKeTvrOPFxlzBhO9YM36aA8/NYbFfD71maZvj5rrULOvbDH1u+N2v94/0oXZDKPGbM11laoa+U/ZIRCSGEEEIIIQzO3DMirfaMRJI9OlhvsuJFNkQKaAnnRUq0rqxbDkrTz4h44WQdbfJIFFfUrSg8NiZi4DbHVmRZplzo6rQKTsmA1IX90sLIgj+/juq7jbiN/aYQTd0SROo8K8Sxc5weyZ8VQamjhh7VJypJgZ43rhhrVKuFrzdECb2gmmwS9lxqAw3s5MX8RNOYy2SqpGlkkaT2pnwUQntUui52Xaz96kJ+30yTRgmsAVPJotebQFJ0LxWf8GYInCfxiaX6Ht/N0Ucf3d1Xn2+9OcyYN4GUFmaSvLlI3QJfKvOq1eJ4MfA+b3/727v7iIjznVx33XXd2JgzSqxzZJG8QQZzxrPoRPRbm+ctBt7nN37jN7r78D1++9AAROqvuWOEOYvP+e8IjsftR4YC2y7Wfvgxtvrt3/7tboysMH7mLfNXug13MiIhhBBCCCGEwZl7RsSjy7RCrdt6Olx5u3Z4u+22k1Tat/E6UtmwalYruKnRar2GNpOotNu1zgD41Xa9oSFtCqViYyKrY44GbgnPvHFcrgHHZ4gGtPSvrf/xL7SWvjEafkw0YexRmafC/YzIqmcvsAHt/tw+dfbCx7A/tvOWj0RbsZnrfacwh5lzbiciTq0NRXn8LNs5+DC2O+6447ox1gLa3Hqr5alsZMga5VF9av88s4gdePxi7YdP0zLao9K8JxrpqWREAJt4jQPRd68bqTfWW6rt3vKWt0jqb0TK+YMM+7XXXtuNTWUTV9btVo2Q24jfJ8udu+9+97slSaeeemo3hu+Rbbnooou6sbHZr3WM2KzVjtdVGmS6sdlibce55g//8A8l9TcQ5tyE77FJrDTOedtqJ9767Ut2yX8/oBBYqv34nfNXf/VXksrvPqnYllbo5557bje20mqOZERCCCGEEEIIg5MLkRBCCCGEEMLgzE2aRSrJ05+kdUlPkSqSSlqStKZLYE444QRJ0m677SZJ2nvvvbsx0lnIFKYiTZgFMgSXoLEjKylPl1/Vu6C73IH0MAWHe+21VzdGKpU2j55anVLBsNSXp5Hq3HHHHbv7aDtJCtd3XffjlvoFtO9617skSb/2a78mqd/Kkte68cYbJfXbF05BXgQugyT9jQxSKoVyzDFvFlGndP21TjrpJEnS7/zO70gq81cq8/2aa66R1C/Um4Lt6vbDfps2k1I5FqRnLkGr55jPadps/+7v/q6k/prHGrd+/XpJJdUuTa9YHdmtVGzl85bj3rRpk6T+XK138nb7HXrooZKk973vfZL6/ozPXnzxxZKkjRs3LvgMY4bjbLUApeBfKmsVtnPpT+0nvn7ie3/wB38gSdpll126MV7jU5/6lCRpw4YN3djYbYeftGSVHBeyR6n4IQXsrRaq4L7H75Xf+73fk9TfbgBp8L/9279Jku6+++6nfM0xUfuLS4X4/eHt/5Gncj7w80R9nC4N5jzLX5e50ob6r//6ryWV3dRbrzk28B0aOrgv8RvWbYzEcjGyQP8d/YEPfEBSkfK6j3Pe/ou/+AtJ5Xw+BMmIhBBCCCGEEAZn7sXqfpVM20SPygPtKY866qjeY6VydciVM1kBqRQc0u5s7FGZxcBxekZkn332kVTsSZRLKtGYdevWSZIOO+ywbozNlMgSeIvFW265RVKJrK6G9rNS8RffxIyNkcgs3Xzzzd0YhWNE7CnS9NsemYGrr75akvTxj39c0sLMylTwCAuZI5+jFMXij5dddlk3RoQP+1AcLEnvfe97JZVWgs5nP/tZSdI///M/S1qY1Rs7rDOegSXy6Zkf7Em0y23HBlb46xFHHNGNEY0+8MADe+8nSVdeeaWkEhmcmu2kYjePDBLNd395wxveIKk0hCCDJpXGHUQEyYJIJTLIWuhr2yWXXCKp2G/sbWeBedoqaiYL7tFRCn1pMuFF+Rwzj/dzxh/90R9JKmum2+f888+XJP3DP/zDU36WsYL9WLN87pKZ8+g8v0XIYnoEHr8l4uxzF/uRkfKN6s466yxJ0n/9138oP5ykAAAgAElEQVRJms45l/WHKLp/bn6TuBKDuejtkIG1kPPJiSee2I39/u//vqSyFrjKAJ/DB6f0Ww9fYy76vMH3PMNJu3EK8z1jiR9jI1QvkvSe97xHUjmn8H6S9Kd/+qeSyho6ZBYpGZEQQgghhBDC4Mw9I+LaNq6YjznmGEklWigVHSr3udacK18iGWxyJUkf+chHJJUrvyldJT8VHINHZ4jk77///gseTwSRSKy3cASih55J+dd//VdJRTs4Zdv5ZyfD4dFBIvxEajwCSGQB27pf8hq85q233tqN/e3f/q2kEjGbqv08uoUO17Wl2Iys3H777deNUQNG9NS1+GRJiP745md//ud/Lqn449g1vjV8Xm+5yLrkkS0yGtS8eQtjIoM8xu1KPR2P8TqGP/mTP5FUssFTs51UjsujzGR2PLLKmob9OHdIJVKPX/JXKtkp5qRvHEdkkPeemv2Ixt97773dfWTFvb4GVcGuu+4qqT//iNDuu+++kvobPqJOYN6SgZOkP/uzP5NUzhlTsh2fFT/zujQ09b72v/Od75RUMhts0icVhQHzGeWBVPyXcy4RfEn6m7/5G0l9VccUYB7xub2+gPOm18axESG+R2RfKufbtWvXSurXXZIdIBPysY99rBv7z//8T0nTyWA62I9zBL4hFRv5uQEVAqoO9xd+K5PxdOUH2SlqYP/pn/6pG/v0pz8taT5ZuGREQgghhBBCCIOTC5EQQgghhBDC4MxNmkUa1Au1SI+TyvOUkrdxlPqFZKQBef6///u/d2PXX3+9pHHurLlcOBZPHV966aWSShrPi4mRLdB21tPl7NB5xRVXSJI+9KEPdWM33XSTpOkUzM3Cj5nCVt/1l7aMpNtJC0vFDyme81aMpDjZxfWjH/1oN4bUYer287lGK+cLLriguw+J1eGHHy6pX0xMChk5kssJ8b3zzjtPUt92FM5Ovd22F4rjI8iqJGn77beXVNrQIoWRivwUKaX7HWveJz7xCUmlIYJUJCJTadXbgvnqbbTPPvtsSf12p7SSRR7oBenIHZBP+hqABAJpxyc/+cluDEnTVO3HnKHZiFSaP3j7XtY1Cv6PPfbYboxjR1rpaxjfCfPVfQ854FRlqFJpKuKSM1ru+tzlXIsUxmXmHD9z2OVCyNY+/OEPSypzWCoS8ilJ2hykqFdddVV3H0X9rXb5SP7cdtxmvXPbIeGiick555zTjU1NzubwfeMbPneR5rosEHkkkmc/N2A/zrV+DmJ+/uVf/qWk8rtR6u/mPjTJiIQQQgghhBAGZ81SrrzXrFmzopfp9SZgnhGh/R0FO371Sws4riLvueeeboyin20ZYdi8efOaLT+qz0rbjqtfIjZeHEfEi0i1RxmJ/pH98CwLV9LbODqzYfPmzWu3/LA+K2E/b3lHtgi7eWEYmz0S3SeqIJXIPdkVb0dIhGE1+p5vREqEmta8RHCkkt3EP++8885ujAJrWtY++uij3RhRsHn73ra0HVEr/EgqWaTTTjtNUr+1L+sffsTGmFLJ/tJqluyStGItU+c+b8kMEQWUpHe84x2SSmGm25bzCOcKj3ATCSQTTBMGacUymIP7nmcfyZD/5m/+ZncfG4oS0fdW+qyNZI/xM0k699xzJRXb+bl4hTKYg/oev0N849pTTz1VUtl8VSrND1qZ8lrx4fYjC0fWwFUhK5FJGvKcgd8QsZekD37wg5Kkt73tbd19/E7hPNLakI/fbm67//iP/5BUzrcexV+hLNKgvsec9cYatCX2bC8KIfc5qJsusDGrJP3jP/6jpLI9wUq3116s7yUjEkIIIYQQQhicXIiEEEIIIYQQBmdU0qyl4Kk8GKrAayzymCW898z/pUGLC+cu8Vjk+3W3XcJVj2E3fG+lfXDMvoedPF1MsSZ2cT9DxlHbcAWZqzSrBTZj3wHf2wbbkT6niFYqDSvGbDtpZeznkiPkM8jYfI8WbIMU1eVXSP/GbL9taTts5vZBBojs2feXopgfCYfLUZHMDFjMPxff83UeP/O9MJADIt11uSr2ohHHhg0bujGkbEOdc+dxzvBzAPvVnHzyyd193EYy6LZGHv7f//3fkqTLL7+8GxvadpqT7/k+eci0zjzzzO4+fI7mE24P9j/6l3/5F0l9aRYywLH9Vk5GJIQQQgghhDA4k82IzJMxR6UnwGgiq1MkvrdVjC4jMiEyb7eO+N7yie9tBVM7Z9SKjTm3Mp6c743JfsmIhBBCCCGEEEbL3DY0DCGEEEIIAaa6meNYmKL9khEJIYQQQgghDE4uREIIIYQQQgiDkwuREEIIIYQQwuDkQiSEEEIIIYQwOEstVn9c0qaV+CATYudlPi+2e5LYb/nEdlvHcuwX2z1JfG/riO8tn/je8ontto7Yb/ks2nZL2kckhBBCCCGEELYFkWaFEEIIIYQQBicXIiGEEEIIIYTByYVICCGEEEIIYXByIRJCCCGEEEIYnFyIhBBCCCGEEAYnFyIhhBBCCCGEwcmFSAghhBBCCGFwciESQgghhBBCGJxciIQQQgghhBAGJxciIYQQQgghhMHJhUgIIYQQQghhcJ6+lAevWbNm80p9kCmxefPmNUt9TmzX8fjmzZtfttQnxX5PEt/bKpbse7FdR+bt1hHfWz7xva0g54ytIr63FSzW95Z0IRLCNmDTvD9A+IUlvrd8JmG7X/qlkuRfs2ZN776f//zn3djmzZubf1eQUdgPm9S3pb7tnva0p0mSnv70J38i/PSnP+3GsNXPfvaz3t8VZBS2k2bbz//Hfvx1+/G4Ae03OmrbOfghf90+9X2/KPN2OWBjt1FrLRwDkWaFEEIIIYQQBicXIiGEEEIIIYTBiTQrhBBGiEtlampJwgAShbngEo7WMc6SeCCL4Xluz1nShKnbsmWT1jHV97kEhtf48Y9/vOB52I7nb+k7mhqz7Dfr+FpjSLL8Nccmi1kJtuQTs+yIfVp2colbmE3LxmP1vWREQgghhBBCCIOTjEgIS2SxEcdfVFoFnfx95jOf2Y3VkVWPyNb2HGskZ2upC6opDpakZzzjGb3HvPCFL+zGfvSjH0kqEUL+lxbaysd4ranYc0tZkLqwlSyIVHyNY33uc5/bjf3kJz+RVHyO//09ea0f/vCHCz7DmAuMFxu1x2b1HPUxHu9+WRcK+2vW2aeWnVpNA8bEYj9TKyO0mNeYVeS+mNcco81qtuYzbu3x5fw8PZIRCSGEEEIIIQzOqDIiXMkSiXr2s5/djREd5K9rBYm6oGdtjU0pmrBYWpFn7PO85z2vG3vWs57V++vRP+zzgx/8YMFYHTWcShR1sbhmnNv43Ate8IJujEjq85//fEl9/+L2E088IanYUZK+//3v9x7TaoE5NeoIvlSipdjnla98ZTfGfS972ZOt2N0G+Co2++pXv9qNPfbYY5Kk733ve5KKLaWFmZR5Myui2YrOue2Yk2Q7Xv3qV3djRPRf8pKXSCr2kopdGXvwwQe7sYceekiS9PWvf12S9Pjjj3djzOlWFHteLKaVp9Su9cAmzNEXvehFC17jOc95jqT++eTFL36xpGLvb37zm93Yww8/LEn6zne+I0n62te+1o2RHfEs01jPLS27trIerfNtnQlx38Oer3jFKyT1bcE6yH3f/e53uzHu88fX7zcl6jnu2Tios05SsSVz2GFtY556jQ73+Tl6tZyTt+SrgI1ZG1v+zDnGbcN9s7Luq41W+97F+GprPZtVt7MtSEYkhBBCCCGEMDi5EAkhhBBCCCEMztykWbUURpJ22203SdJpp50mSTriiCMWjJHW9RQb8o2vfOUrkqTLL7+8G+P2l7/85d5jpfFJPBZLK5W+6667SpKOP/54SdIxxxzTje2xxx6SSprXjxfJC/Zx21199dWSil1Ju0vTtZ1U5AbIYiRpp512kiQdfPDBkqTjjjuuG9trr716z3dpCP6I/OXSSy/txm688UZJxbZIPaRBd4ZdNq3UeMt2yFte97rXSerb7rWvfa2k4i8uGdxuu+0kFbnLRRdd1I3he/fff7+kvhyh1VJ0LHacJclq2e7lL3+5JGnPPfeUJO29997dGNIXZAUuFzzggAMkFUnStddeu+B9sNO3vvWtbsztOBYWW6zLcXnhNDIh5IAuzcK/eDy2lqSDDjpIUpn3X/jCF7qxm266SZJ09913S+rLGFrShHn63ixZ2yxpi1TWsZZ0DekLf1/60pd2Y7vvvrukMu+/+MUvdmPc3rRp04LPMDbbPRXLtan7JbZFhuWNJpCp4rMuSWWuIk11VosMy2lJ12r5kJ8zWAO33357Sf21jd8y3DdG31opZkmAvUEMcxybeiMOzg0ug571PtvSvsmIhBBCCCGEEAZnbhkRIgX7779/d98HP/hBSdK6desklateqVx9EbnyK2EiOvvss48k6VWvelU3xu2PfexjkqQvfelL3ZgXFk8JbEcUVZLe//73S5KOPfZYSdKOO+7YjWG7DRs2SOoXZhI1JOpP8atUiunOO+88Sf1oKlfSU4k6+JU8kSsvqj711FMllYwSWSSpHCORUs9sECnktU488cRujKzH+vXrJfX9bQoZpVnFwR5dJgPCXyKm/hobN26U1M9mkMVjnh9yyCHd2KOPPiqpZJM82ogfjjlC2LJdXXwulcwGx+5jZEIoRPeMJI/bYYcdJPW/D+zJdzVrY8QxsNg5gA+wZkllnSOz4fbjuMmCt1r78t6tTB3v5w0WpjBvW9RFvlI5ZtZ5CvilfnG61M/G4Wt1dNWfx/v5fJ9CFlhqZ+W47etQrUzw9tB19s5ttMsuu/Se5wX93/72t3vvN0X71bSyxLU9PUtcN0jg950k7bzzzpKKH/s5ldu8tqtmxtxue7HMKuh3v6x9zzOdZOO4z7Nx9eabrcL+lfK9cZ+hQgghhBBCCKuSQTMifkVH1O7MM8/s7jvqqKMklSvg++67rxv75Cc/KUm64IILJPVrPV7zmtdIkk4//XRJRasulUg/UQhaM/rnmUKEwaOaRP3OOOOM7r4TTjhBUolque0+/vGPSyr1C57ZwC5kBIg4SNJ+++0naWGtg7NSmsFtjX9OontvfvObu/tOPvlkScUenjk799xzJZW6BY+QEv0/5ZRTeq8tSfvuu6+kkg3wNqCtzzVW+7Vs5/VbZELIbtI2VpIuvPBCScV2HmlFp8/34JEbMlL43pY+1zxtN2vTPZ+32I66GalkRPAVj46SgcN/PGJNJo756n5HloSI1thbVi7WfnUraKnYkjXf1zbqFKhx86ghr89a6pFnzj9kfVsZkbGwWNsRcfbIPFlcMpOeMcJnWLO89gbfw3Zun3vuuUfSwg03pfHZbku0suieUWI+UsvlY8xHlBuefeecUWedpHKOndUyf4xzuGbWpqNS8UPs5FkPbEWtAvaVym895rm/T10b0rLTFGxX08oo4WvYze2HjcgQ+XmDDDL28zXRfzPWrLTdkhEJIYQQQgghDE4uREIIIYQQQgiDM6g0y1OXBx54oCTp9a9/fXcfqadrrrlGkvS+972vG6Ngsy6okYqMplU0Rnqd9nmeDp1SsbrbDjmCy2OQLdDG84//+I+7sXvvvVdSSZd7ipSiYNLsSIykIldgzD/D1IrV/bNT2Oq+hzzh5ptvliT9/d//fTd25513SiqpYk9nfuMb3+g937+TWv7hbS5hCvZz2yHLoM2x36aRxEc+8pFu7IYbbpBUCvw9hYz8gOd7m2RkIj6Xa6ZmO2QIbru1a9dKKn50ySWXdGPYDsmBN++gnTnNKUjHS9L1118vqcg6XB4z1l3Anwq3H76DjE0qckCkR5///Oe7sVtvvVVSkbv5a+FfyEB8DHsx3122NYVi9VYBK/ah5a5U5I80dHHpKDIN1nmXbbEGsI66f0FLmjUV36t3nJfabYyxJWu/ywIpAuY78AY6rHP1TuBSOf+0pFlTkLa1ZIHY0XeSRy6EFNrXeXyH+c5vRan4LOuly07vuOMOScW/3F5j97malrTNC/qxJfbzcwMgw0L+KxVfRQLo3xOt8nnvIf0tGZEQQgghhBDC4MwtI3LYYYdJ6l+pErmnjS9ZEKm9iVk9xlUeLfCkEo1uXUFP6SrZC3kPP/xwSf1Nkh566CFJ0t/93d9JKraUSuanFSnAdkTDvAkAbSyJyE4hIlPD1b3bD98jqiyVglbaPPsGZ0TzZ9kPn3M/pSCPsZb9xlJw3YLP5lkMItA0MpDK3GJDws997nPdGBs9tiLJ9Zx23yNKiF+2IqtTsJ1H+ogg+6aFHPMtt9zS+yuV9Yxj9wgtkGXBf6XyfRCVnZrtpBKpc/sRRSWqJ5WsBdlGWpRLpTEJvucF/Rwz5wc2L5SKz2LHVsR/zPbjs7UiqB6Zx8b4DoXmUrEdj/EGAZwXaJRw++23d2Oslfj1FKPS2K+VjfP20NiBjJvPQW5z3vG1jfbH/PVzDWsiWYGpnXPrzQilYsfWXGZueRtZbEWDBI/a47+sAQ888MCCz0Bmair+5rTs12rDix34jeH+BTzei/2xG38feeSRBe8zj01vkxEJIYQQQgghDE4uREIIIYQQQgiDMzdpFikoTylRaPjYY49JaqfE6+dLJQVNusmfR9EdBe0U30nTSN215DEUJrkNbrvtNklFyuLptfo4/Xmk77Cdy2VIzyMRmZrtpHKs3j+fPu7eXxtpBrvOzzpWtx/pZl7T7UfaGKkH6XZnzHYkJe6+R6MEZEZSaXiA7XwvjFqS5bZDmsD34JLKu+66S9JseYxT7zo7L7vWqXUvGGaOecErtsKGXjCML7VkbdgOaZLve8Na0JLHzNqddwzFxHXPfJdlYDe3KcfIXHOJRy1J9TmNz7Hnje8vhe8ttiEH3/W8C9lrm7mdWP98HiG55Th9n6gnnnii95re2IXzD8/zc3gtB3RbzPK9MUiQavv57xV+Y/j6ztrEOdSlWdiW78DXNmRuNM5xG/G8xe4Ezmedt/1q27kskOP0Y6rtgy2lYiv8isdK/f1YpP65CZ9drC3GtI/cLN/zeQzYhHUMSaRUzsPc5/ajqQk28t9AnEsWa49tab9kREIIIYQQQgiDM2hGxK+IiWB54SbRQSIMXpzj0SypX/jEa+yzzz6SypWdJK1fv17S4rIsY8ZtR/SOY5JK9KC102YdIfDsADsSE631yM1VV10lqR2VHkMUYbkQISByIJWIHwWI7l918Zu3IaSVKBFq31WcNqpEaqbWKKEVleZze4QU+xDFabVJBS/2pPAdW3uhNq1X66zAVMB23i6ceef2xCeYW63sBTb06BVtLIEW01Ip3G5F6GdFpVsM7af1Mbsv4Se+DnGbOdYqtMTeXqxO61Xsj79JJYM56/O17muNDWm/uu2s+x5R1VYrYtZ3P29il1YbbdY6jtd9z7Oh/pj6dv2Zx5CNq3cA97WLz+7+xTmXc6afO+tGHP5bBj/Ext4kYNaWAovxvXnZb5btZjV5Ya75uRjlQOv8w+8aXtOb8tS/EZ3FZILnSX2sbr/WOs66RxbTsx7YAf9yu9Stvb3RxCz7rTTJiIQQQgghhBAGZ9CMiF/t0+LT9dJEXfbcc8/e/1K54uM+b71KK1GuEj2yyvsQrRjD1e9y8GjVxo0bJfVbKnKl26qTIerA1bbbjs3ViOR7dAutOVrDqUWlHfc9jmuHHXbo7iPSRbSPmg+pRB2IMLCJkFR8lRoTXlsqbRmJ1rrvTcEPW5Esahm87R/RLCL+3i6w1jBTYyIV+zNHycBJJetHdKxlrzHasP5MLX2vR+dqTbhHsYmiEh3ztrVosIlosSZIZb7ymovNgoxJr9/SmrPe+bpHBJ5onh8XduM1fNNH1gP8zOdtXcvV2lys1Uq5FTWfB612n9R1+MZn1CNxbmypBchI0apXKueiTZs2SeqfM1obDkMrU1PbbFab/qHAX1w5QKbcbYodWnWFzCH8xc8Z+BeZEI9Kz5qzLftxH3abl/3qNvmuGmh9bmzQysbV5x2vC+H4OLf6Bqb1utVaC3w95r7W5ptDU7fc9t++rQ1KOVZqsvz3DWPY27d54HFsXujtzmfZr7XucR9zd7F1TS2SEQkhhBBCCCEMTi5EQgghhBBCCIMzqDTLU9bIhXxXUSRDBx10kCTphBNO6MaQcCHn8DQQrSsvu+wySdIdd9zRjc3a1XoKtFpPIo+hzaRUUucU7vvO16SYKZLzFCkF79dee62kvkShlnhMEeznqV9kRb4rK+1okWR5UTWvQSrSx3hdil09VdwqUp8SHLe352Te4oNSKaJGMkMDBKlI1/A5l2ZhF+arSzywK48ZowyrRS2p8HWnVTCMjA3ph8sQ8B/mLbaUil3wt4ceeqgbY51drCSLz9gqDp3XuomtXFbAbsIu50U6xFx2X8V+vIbbFgkJ8hgvlq0/g/uer51Qy2laRaUrRasYHHmHy7BobuBSN2SoHLv7Jbd5DXa5looNkHf4ualuHdwqNPb76jb+7m9DSGVa8h18w5tDIK3Cp6RyzmAOtiR5+Ko3SuAY8T23ey2BabVg9vv4PjmP+9iQvlc3NXB/Yd55e2iaReBD/lnr86y/FnMNKfQs27Uahfh9fFbkme57Q6x7s+zH+UAq5wiXQ+NXfHa3A2si656vl8C51mWos1rPt+zHPOEz+PEs9XydjEgIIYQQQghhcAbNiPhVJtF2j0RxtUVmxFv70uqTqIpfXVN0xxWxRy2mGo2u8eOgsNzbTNY28w3n6japHj298sorJZXvxjMHU80iOVyZe7SKjad8EyUKWbGfRxO5jyt+j0xcd911kkqxcKs95pjaBC4HPybmnbfpJHJFFs4zIkS+wCMqdUS15W+tDbumYEeP+kKdWZNK9ArbeYFsHQE95JBDujEioayjblduYzOPNs4qSB/DfCcaxzpP1F4q2Uov/GUOH3bYYZL6RZ7AfYceemh3H3ZgLfXiWs/CSP2oIZ/P1xPWZl875wE+QQSU1uJ+24+NYycb8eCDD3ZjHCfrIK3xpTJfiYR6q3he3zMvwPfQijzzmq15MxRkLTiXur9gB89scB6oC4elMq/xVc8Es57SZtvtRyScv24PPoNHqvE91uMhC649Ao7PsY65IgOOPvro7ja/QVirfCNX5iKv0cqi46uttshkstx2nId8nWQO0zDE2y8PvRayzqEG8vbsrD9HHnlkdx/2w8/8twznY9ZEPx8DGaXW3MXP3Fb8nvR5zZzFfr6pYjIiIYQQQgghhNEzaEbE4SrPo/pElFotA7n6JprgESkiLVzdeTQb7eRqyYxIxU4eRcCeRBM8glVrc1ttHYnAuB6TiNlUN4F8KogeeZ0D9iKasHbt2m4MfyQS5dGSHXfcUVJ7I0QeP1X7tSL4ZDCJhkqlbouoyV577dWNEVWpN6KSyhymtsSjjURZsN1UWh/XOltfizhe19QThSLq5/OWNQuf9JbSZPXQX/v7sBbwXfnax/o5a3PNeW1iKJU5g08RIZRKZtKzJNiPdctbHGNn/OqII47oxrAD89dty3uz/nlkkPOVf4fYmXkyZG2D2445RubsTW96UzdGdN/XfrKbZEt8Yz18Fb866qijujHOP9jJa5f4jrCB11kQMfUMK/WdrLFDZZVa9VD4ycknnyypf8ysbb7BJhkNjue+++7rxphD1DWtW7euGyN6jW3cfnXLbvd17vPzDy2UWY89qr/SuO/hJ6effrqkEo2Xym8Sn0e17TgOB7sccMAB3X385mG+e9aPcy829Gw8c8M/M3Vl/G3Via0k/ln4ns844wxJ/d8fHI/7KvbCfv5bBj9h7XT/Ys7j661aYn7LuKqG79AzImRVqPPeGt9LRiSEEEIIIYQwOLkQCSGEEEIIIQzO3KRZpC49HUa6jvSRyz94HGk0TxGR8iX9jBzLX4Pnj6Egczm4XAJZBdIBqRTOIR1A2iItTN+1dsc8/PDDJfXlXhTfkWafsrytZT9PJeIzFG62pCvY1lPMyIooJHPfw+ew37x3XF4qrcJmjsF3csXXkMV4e2OkDNjQfY8CxwMPPFBSmf9SkS9gu5ZkY4xzGZtx3O4rzDWXq3AfaXBvMYv8BwmMFxZiD+QHLknCh5E5efMOnuf2rFv6Di3NcskB6zrHTOGwVOaaHyvzDakGz5MW+qXL15A21EW2UikERbbV2vXZm6VgP75rl2atlI/yOdy/OBZkft7cgKJpL8rHZtiJNqFS8T380p9H60/8mHOHVGyFzMPXUWyOrFAqPopvD9VoBvt54TfyPKQsbj+kLP547I3U16XkfHYkNy7pQtKC/Y877rhurG7j6usl52O3Ed9LSzqz0jI39z1+e+F7XiDN41qF96xVPs95PPbx9+H3HJJMZHR+X6tRQn0+kRa2Vvbn+fltpfDjOuaYYySVc6EX6LdkhMxVfMEfjy9gD1/fWbew7a/8yq8seE3mov9e4X18PUN22NoWwuWXiyEZkRBCCCGEEMLgzC0j0oq2chVK1sPbgRHNpzDGo1sUeNLyzNv+UgxH1GKpV2pjwa9quTL2iBG3idrfeOON3RiR+dtvv11Sv4UjV8GtTdO4em5lCaZGy34tX2gVz3EffukFn3VRobfdI4JFdNHtN8ZoPtSFsB4FJOrhGTdvbyr1s5wcM4/312K+tyLdRLBo8evRmdb3Ns8Wya2NqbCTHy+4bxG9wn98Qz4idGTufD3EdviiZ6GwIxlTz9IREfRiaz4rNvbI2xBzvlVwTQTZI8LYwdvMst6RSfIocL2BpGd7sR/H7nPa2wNL/ULQ1vfKc3kfjwyuVOF63RRBWlhs6u+N73g0ncwE0Xf3PcAn3AY8rhV55r35Ht3mzE2fo6yD/HXb1evKtqTOXErF7/lt4fbjmH0dwh/5Dvzz1m3jPYOGz9bF1VL5DrGtt0nnffw7ZI4zn90fVhrP0DKfWIdav018bvoxSO01HRt6phw/ZN30dQ/f43n+HtjFvwcynzn/iukAAAxQSURBVMyNoVtHu/3wpbp1uFR8yOdnne1qKTj46+s/v4d5H8/O1+uG+38rE8xv8W1R5J+MSAghhBBCCGFwBs2IeOSLK36/IqNNGxEJjxxeeumlkkqExSMZ1EdwlYx2TSrRrbvuukvS6siIEEkhkiyVVm3omj0S9alPfUpS0fa67dBG0+rRX5NN/ohAtiI+Y6cVOcRPPKvGpo9o7j2acsEFF0iSrrnmGkn9yAn2o40gulOp+B6RR49CTwGivh79Zd66P5JVIzrltrv55pslSTfddJOk/vxDk42u31tHo03Hdp4NmNXSd96bR+JnzDE/XtYp/2xoe5mvRKX8PtZBj8YSRW3VHXmkzT+TVCLOLT10a7O+oeFzsdYQdZNK1NCziWRyiW76hpD4DPPW/auOOLt/8fpErj3i19p8E3vhe/OyH987bXg9usqa5xufAeuZt8/Gb6+//vre86Xiv9jO7cO5ifv8/Yimuq3JlGLXodbIVnYG+6Em8DHWQI8I11ltr7FhTl111VWS+hFrotnYz+sR8F/83+tAWQ98TcHvmRtDbqrpx4/tmGutGgvfSJnPyZrjWUhe94YbbpDUtzn+hJ94y2l8FNt5NolMin+ntFvGrkNvSOqfhfWI+eb243HeHprvm/OF2w/b3HrrrZL6aygZEV6f87NU1gvWL1/HqM1x30Ot0PLLpZKMSAghhBBCCGFwciESQgghhBBCGJy5SbOQCHjLO3ZkJtVz2223dWOzWsmSrqMox4vVkdoMWcS1ErgciDSxt/pEjkbKDSmMJN1yyy2SimTGpVmkPSkIO/jgg7sxpCReQDhV3PfwHbcDx0gq/N577+3Gzj//fEklPekyBSQypFa9DWFdlDj0zq3LhVQwdmrNtVbraGzgKftzzjlHUmlZ6XI4vhNS6d56FT9GFugtP8eGp9hJT5M69zHmmssfsR3pc6SVUvE3jt2lNkgAW22R8U+kNryHVNL2rULEp/p/pfHPwhqOD3kLdx7n0jPkVtzn8ivWSZ7n0pl6t2IvBK2/Q5dEICXyxyNh4H2GaETBd+RSJqS3fMYNGzZ0YzyuJQfBBl40jR2xoa95dYG/rwW8D/bxecuYy05qSdbQTTz8e7z22mslFZ+75JJLurFWkwfsxnnEz9HM1ZbUDFsiw3LpEedo3s/lRdjG7cfayX1D2s8ldhdffLGkct48++yzuzHWeT8W5gznRofzQN0sRyq2Y763mihgA5cK1ecav83fodc93z6A3xicJ/03F2u725vPjBza11Akz3wX/HaW+nNc6hey177q530+g9u0JeFaLsmIhBBCCCGEEAZnbu17KVJft25ddx9Xu7SZ9WgYV7mtzV2ISLCJjhcMc1U35napi8Gv1okieIESV6pkNjybxNUzNvPsAK9LZsptx1Uwf1vPGzt8To8Y1BuQSSVqTATPM0pE/Fptkylww24etSbiyPflRd/445jtyHF6RApf8CgojSDI+HjkiwJFjtNfiwgP7bfddhThEcX2QkciMD6n62J1Z0gb1zbz6CXrlBdF8jiif96gg+goNvfjxZfwO4908RmwnUfE+Dwe/apbqXt2ZaXazz4VfD7mY6s1sn8+jrvVxri+z1+Lcw2v5d9J3Ta2lU33KGDdgtYfv9LnHfdt5p9HWqG19nMfvuTRaeYf52lvAoDtODbP9NYFqz7f8UufE7XtWvN3JXH7ocQgk+2fpfW5sF+dGZEWtqH2c02dQfH1sm6X3crAuH/W9huSVra3lbmu28G37mvZp/U+zC1s4fap13m3Zevc3WpYMC9Y7zw7Bi3fm9VinwL0xfjGrPXd7YL9Wq23t4X9khEJIYQQQgghDM7cMiK1Dl0q+mg25PLoVt3uzfW+73rXuyRJb3zjG3uvLZXowWrKiHBM3hqRaDI281aMRLW4ena7nnjiiZKk448/XlI/8kX7vFY0bWr4948vefSG6HOrPS2bzeGrHrmnbfIRRxwhqV/nQGQSO3rUp6WrHEsLWmjNUaKZrbaS9cZ4UrEVf90vme+0sPUMHz5KhMjb2mLXWRG2eduuFS0iWuyfm6wQtnMfqSPWHmnnNlFs3wySKCFZJFo2SsWeLfu0ooZD0/I5wKatjUFbjydyj/1a3wXP93lbbyTpNSmtGsW6fe+8zjWLsZ3D5511TPz1jBv2INvRau2LD3qklsd7FLZeB+c5b5caIa/XHD8WztHM09amsDzeW2nX36GvFa2Ncevvel72W252oWW7ulahlcXDn92u9Wdw1Qyv34roj4nlfia336zfafX50W1Uf3c+xpz1196W9ktGJIQQQgghhDA4uRAJIYQQQgghDM6g0qxWYZ23SaVoFanGBz7wgW6MNm3IjChMl8oO4Mg3aPkpLWwHOVXcdkgGKBKWSqtGbPGe97ynG0Oige1cxkGROlI3l9wgYWoVNI1FArNY/HMiL/IC6KuvvlqStOeee0rq+xetLJFouYyD9tC0ZHVpTV0o35ISTcF+LVmgF6jih8iM8EGpyA4oqnbb0eoY2YdLYJivyDlatvPUcS2FGotd/XMgo/CCXo5v1udmXfM1DElWLe2Sin+TrvexlqSB73TsPtlaw5lbLfsh8WAdc3kR9msVGtftbN1+LclMLfsYo/1mfSbs6vIOjrMlsUJuxGv685jv2M6bALTkivX3N6XzdP19u0/UMsyWvKjVSAVfw96thhx+X22/qdH63LOkobXsqFXIPksiuVLF1mNi1vHUY26j2rYt+7X8cVuQjEgIIYQQQghhcOaWESFqxwYuUmlhtnbtWkn9yGrdDo/iYqls4MRrnXXWWd0YmYKh21Bua1qFlrTqlUq7XiL6FFFL0kknnSSpZER8YxwiXrRKPvfcc7uxa665RlKJavlnmHIUgYiLR/XvueceScW/vKh6//33l1Q2e/R2i9gUf7ziiiu6sc9+9rOSSjF3q3VgizHbloiIRzrJOtYbwUklU7THHntIakdPea3169d3Y8zlBx54QFJ/vreKa2HMtmtFkOvNt/w4sQ9zlHVOKhFnsiVsZuevz32tTb+8hSqRsKm0Om9F5VqF2nWLT4/4Mb9pouCNK5jTFPn7eomvTtl+MKvdKbeZa94ylmg9a2WrFTT3efaP12htNDfPBgnbitba07oP/6gLr6WFa5uPtTaYnfU+q5FZDV3qbOUs24cnWarfrJSfJSMSQgghhBBCGJxciIQQQgghhBAGZ81SUi1r1qzZZnmZ1o6aFLIeddRRkvoF14Ashr0ZpJI637hxoyTp/vvv78ZIoW/L1O/mzZuXvKHGtrQdeLEuvdzZi+GUU07pxii2JqXuMg6+B5oGuFQOKUjdc38r2bB58+a1S33SStjPqfdl2GmnnboxCvnxT5dqIPHAVi6XY88GfNDlDcstbB2L77XAhl74y740SGC86BXZFr7rchd2GEdK5HKvVoHmIu24ZN8bynb2ft3teg8Ctx12rW0oFV9EooV0Tipz2aWqiywUHuW83cJ79/76eslt7OZSzFri4VLMluSoft5TMHrfm0VrbwLO3T7fa1u7T9V7k0ir1/cWQ2t/rpavQkt+uJjzyJjPGduS2kdbPjvEOeP/v/fk7FezJfsthsX6XjIiIYQQQgghhMGZW0ZkC++zxfsWWxi2Eow5wtCyXb2z8KwirgFsOOoIwyz7EaFutbybtevztmTMvtcCm9UtP6US9eOvR+lXqPB3UlHpOqLfipwSlW4VbfJ3S3N6tUcGZ51PmNutHYNba2KrEHaR2c1J+d5iqNulSrNt0foeFnnemazvzWLWDtizHt/a8XoWUztnrAQt2y1SJbMqfW+5tFrmzyIZkRBCCCGEEMJoGbR972KZZ7Zj6rTs5JrcMJtZ9osdl86slpNhNouJtE+9LfkQzDqftGo9wuJobe6Y8/TiWW7r1LSgXTqt+pqwdFbK95IRCSGEEEIIIQxOLkRCCCGEEEIIgzNKaVYIIYQQxk/kWCGErSEZkRBCCCGEEMLgLDUj8rikTSvxQSbEzst8Xmz3JLHf8ontto7l2C+2e5L43tYR31s+8b3lE9ttHbHf8lm07Za0j0gIIYQQQgghbAsizQohhBBCCCEMTi5EQgghhBBCCIOTC5EQQgghhBDC4ORCJIQQQgghhDA4uRAJIYQQQgghDE4uREIIIYQQQgiDkwuREEIIIYQQwuDkQiSEEEIIIYQwOLkQCSGEEEIIIQzO/wOcbGSVFXTXVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x504 with 50 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_interpolations(enc, dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tsne(objects, labels):\n",
    "    from sklearn.manifold import TSNE\n",
    "    embeddings = TSNE(n_components=2).fit_transform(objects)\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    for k in range(10):\n",
    "        embeddings_for_k = embeddings[labels == k]\n",
    "        plt.scatter(embeddings_for_k[:, 0], embeddings_for_k[:, 1],\n",
    "                    label='{}'.format(k))\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latent_variables = enc(test_data[:1000][0])[:, :d]\n",
    "latent_variables = latent_variables.data.numpy()\n",
    "labels = test_data[:1000][1].numpy()\n",
    "\n",
    "plot_tsne(latent_variables, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semi-supervised VAE model\n",
    "\n",
    "This part of notebook is inspired by [\"Semi-supervised Learning with\n",
    "Deep Generative Models\"](https://arxiv.org/pdf/1406.5298.pdf). We will also use this model to illustrate the Gumbel-Softmax distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the semi-supervised setting, the generative model is a little more complicated. In particular, it incorporates a new variable $y$ that represents the digits class.\n",
    "\n",
    "\\begin{align*}\n",
    "& p(x, y, z) = p(x \\mid y, z) p(z) p(y) \\\\\n",
    "& p(y) = Cat(y \\mid \\pi_0), \\pi_0 = (1/10, \\dots, 1/10) \\\\\n",
    "& p(z) = \\mathcal N(z \\mid 0, I) \\\\\n",
    "& p(x \\mid y, z) = \\prod_{i=1}^D p_i(y, z)^{x_i} (1 - p_i(y, z))^{1 - x_i}\n",
    "\\end{align*}\n",
    "\n",
    "Typically, whenever we train a model with partial observations, we interpret unobserved variables as latent variables and marginalize over them. In this case, the loss function splits into two terms: one for observed variables (we denote the set of indices of observed labels $P$), another for unobserved.\n",
    "\n",
    "\\begin{equation}\n",
    "L(X, y) = \\sum_{i \\notin P} \\log p(x_i) + \\sum_{i \\in P} \\log p(x_i, y_i)\n",
    "\\end{equation}\n",
    "\n",
    "Again, we can't compute the exact values of marginal likelihoods and resort to variational lower bound on likelihood. To compute lower bounds we define the following variational approximation:\n",
    "\n",
    "\\begin{align*}\n",
    "& q(y, z \\mid x) = q(y \\mid x) q(z \\mid y, x)\\\\\n",
    "& \\\\\n",
    "& q(y \\mid x) = Cat(y \\mid \\pi(x))\\\\\n",
    "& q(z \\mid y, x) = \\mathcal N(z \\mid \\mu_\\phi(x, y), \\operatorname{diag}\\sigma^2_\\phi(y, x))\n",
    "\\end{align*}\n",
    "\n",
    "### ELBO for observed variables\n",
    "\n",
    "Similiar to VAE:\n",
    "\n",
    "\\begin{equation}\n",
    "\\log p(x, y) = \\log \\mathbb E_{p(z)} p(x, y \\mid z) \\geq \\mathbb E_{q(z \\mid y, x)} \\log \\frac{p(x, y \\mid z) p(z)}{q(z \\mid y, x)}\n",
    "\\end{equation}\n",
    "\n",
    "### ELBO for unobserved variables\n",
    "\n",
    "\\begin{equation}\n",
    "\\log p(x) = \\log \\mathbb E_{p(y)} \\mathbb E_{p(z \\mid y)} \\log p(x\\mid z, y)\\geq \\mathbb E_{q(y \\mid x)} \\mathbb E_{q(z \\mid y, x)} \\log \\frac{p(x, y \\mid z) p(z)}{q(z \\mid y, x) q(y \\mid x)}\n",
    "\\end{equation}\n",
    "\n",
    "### The final objective\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathcal L(X, y) = \\sum_{i \\in P} \\mathbb E_{q(z_i \\mid y_i, x_i)} \\log \\frac{p(x_i, y_i \\mid z_i) p(z_i)}{q(z_i \\mid y_i, x_i)} + \\sum_{i \\notin P} \\mathbb E_{q(y_i \\mid x_i)} \\mathbb E_{q(z_i \\mid y_i, x_i)} \\log \\frac{p(x_i, y_i \\mid z_i) p(z_i)}{q(z_i \\mid y_i, x_i) q(y_i \\mid x_i)}\n",
    "\\end{equation}\n",
    "\n",
    "Again, we will use reparametrized Monte-Carlo estimates to approximate expectation over $z$. To approximate expectaion over discrete variables $y$ we will use Gumbel-Softmax trick.\n",
    "\n",
    "# Important practical aspect\n",
    "\n",
    "ELBO maximization does not lead to any semantics in latent variables $y$. \n",
    "\n",
    "We are going to restrict variational approximations $q(y \\mid x)$ to the ones that correctly classify observation $x$ on fully-observed variables $(x_i, y_i)$. As in the original paper, we will add a cross-entropy regularizer to the objective with weight $\\alpha$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{1}{|P|}\\sum_{i \\in P} y_i^T \\log q(y \\mid x).\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RelaxedOneHotCategorical\n",
    "\n",
    "In the probabilistic model defined above we are going to replace categorical prior $p(y)$ and categorical variational approximation $q(y | x)$ with Gumbel-Softmax distribution. The distribution class is implemented below:\n",
    "\n",
    "For more details see [Categorical Reparameterization with Gumbel-Softmax](https://arxiv.org/abs/1611.01144)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelaxedOneHotCategorical():\n",
    "    def __init__(self, logits, temperature):\n",
    "        self.k = torch.tensor([logits.shape[1]], dtype=torch.float32)\n",
    "        self.logits = logits\n",
    "        self.temperature = torch.tensor([temperature])\n",
    "\n",
    "    def log_prob(self, x):\n",
    "        log_Z = (torch.lgamma(self.k) + (self.k - 1) * self.temperature.log())\n",
    "        log_prob_unnormalized = (nn.functional.log_softmax(\n",
    "            self.logits - self.temperature * x.log(), dim=1) - x.log()).sum(1)\n",
    "        return log_prob_unnormalized + log_Z\n",
    "    \n",
    "    def sample(self):\n",
    "        gumbel = -(-torch.rand_like(self.logits).log()).log()\n",
    "        sample = nn.functional.softmax((self.logits + gumbel) / self.temperature, dim=1)\n",
    "        return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An illustration for Gumbel-Softmax\n",
    "\n",
    "- Temperature allows for smooth interpolation between one-hot categorical distribution with low temperature and a $(1/K, \\dots, 1/K)$ vector with high temperatures\n",
    "- The exact computation of $\\mathbb E_{q(y|x)} f(y)$ requires computation of $f(y)$ for ten possible labels $y=0, \\dots, 9$. On the other hand, with Gumbel-Softmax relaxation only one sample $y \\sim q(y | x)$ is enough. Therefore, Gumbel-Softmax gives almost a ten-fold training speed increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "n_classes = 4\n",
    "logits = torch.randn(1, n_classes)\n",
    "temperatures = [0.1, 0.5, 1., 5., 10.]\n",
    "M = 128 # number of samples used to approximate distribution mean\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=len(temperatures), figsize=(14, 6),\n",
    "                         subplot_kw={'xticks': range(n_classes),\n",
    "                                     'yticks': [0., 0.5, 1.]})\n",
    "axes[0, 0].set_ylabel('Expectation')\n",
    "axes[1, 0].set_ylabel('Gumbel Softmax Sample')\n",
    "\n",
    "for n, t in enumerate(temperatures):\n",
    "    dist = RelaxedOneHotCategorical(logits, t)\n",
    "    mean = torch.zeros_like(logits)\n",
    "    for _ in range(M):\n",
    "        mean += dist.sample() / M\n",
    "    sample = dist.sample()\n",
    "    \n",
    "    axes[0, n].set_title('T = {}'.format(t))\n",
    "    axes[0, n].set_ylim((0, 1.1))\n",
    "    axes[1, n].set_ylim((0, 1.1))\n",
    "    axes[0, n].bar(np.arange(n_classes), mean.numpy().reshape(n_classes),\n",
    "                   color=cm.plasma(0.75 * t / max(temperatures)))\n",
    "    axes[1, n].bar(np.arange(n_classes), sample.numpy().reshape(n_classes),\n",
    "                   color=cm.plasma(0.75 * t / max(temperatures)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SS-VAE implementation\n",
    "\n",
    "The computational graph for observed labels has the following structure:\n",
    "\n",
    "![computational graph ss vae xy](ss_vae_xy.png)\n",
    "\n",
    "The computational graph for unobserved lables has the following structure:\n",
    "\n",
    "![computational graph ss vae xy](ss_vae_x.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes, d, nh, D = 10, 32, 500, 28 * 28\n",
    "\n",
    "yz_dec = nn.Sequential(\n",
    "    nn.Linear(n_classes + d, nh),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(nh, D))\n",
    "\n",
    "y_enc = nn.Sequential(\n",
    "    nn.Linear(D, nh),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(nh, n_classes))\n",
    "\n",
    "z_enc = nn.Sequential(\n",
    "    nn.Linear(n_classes + D, nh),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(nh, 2 * d)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss(x, y, y_encoder, z_encoder, decoder, T=0.6, alpha=32.):#, verbose=False):\n",
    "    ################################################################################\n",
    "    # NOTE:                                                                        #\n",
    "    # hyperparameter alpha was tuned for the implementation that computed  mean of #\n",
    "    # elbo terms and sum of cross-entropy terms over observed datapoints in batch  #\n",
    "    ################################################################################\n",
    "    y_is_observed = y.sum(1, keepdim=True)\n",
    "    # sample y from q(y | x)\n",
    "    qy_x = RelaxedOneHotCategorical(y_encoder(x), T)\n",
    "    y_gumbel = qy_x.sample()\n",
    "    y_to_decode = y_gumbel * (1 - y_is_observed) + y * y_is_observed\n",
    "    # sample z from q(z | x, y)\n",
    "    qz_xy_loc_scale = z_encoder(torch.cat((x, y_to_decode), 1))\n",
    "    qz_xy = MultivariateNormalDiag(\n",
    "        qz_xy_loc_scale[:, :d],\n",
    "        nn.functional.softplus(qz_xy_loc_scale[:, d:]))\n",
    "    z = qz_xy.sample()    \n",
    "    # compute the evidence lower bound\n",
    "    py = RelaxedOneHotCategorical(torch.zeros_like(y_gumbel), T)\n",
    "    pz = MultivariateNormalDiag(torch.zeros_like(z), torch.ones_like(z))\n",
    "    px_yz_logits = decoder(torch.cat((y_to_decode, z), 1))\n",
    "    px_yz = BernoulliVector(px_yz_logits)\n",
    "    \n",
    "    loss = (px_yz.log_prob(x) + pz.log_prob(z) - qz_xy.log_prob(z)\n",
    "            + (py.log_prob(y_gumbel) - qy_x.log_prob(y_gumbel)) * (1 - y_is_observed)).mean()\n",
    "    # compute the cross_entropy regularizer with weight alpha\n",
    "    loss_supervised = (\n",
    "        y_is_observed\n",
    "        * (y * nn.functional.log_softmax(y_encoder(x), dim=1))).sum()\n",
    "    return loss + alpha * loss_supervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain\n",
    "\n",
    "def train_model(y_encoder, z_encoder, decoder, batch_size=100, num_epochs=3, learning_rate=1e-3):\n",
    "    gd = optim.Adam(chain(y_encoder.parameters(),\n",
    "                          z_encoder.parameters(),\n",
    "                          decoder.parameters()), lr=learning_rate)\n",
    "    dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "    test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=True)\n",
    "    train_losses = []\n",
    "    for _ in range(num_epochs):\n",
    "        for i, (x, y) in enumerate(dataloader):\n",
    "            total = len(dataloader)\n",
    "            loss_value = loss(x, y, y_encoder, z_encoder, decoder)\n",
    "            (-loss_value).backward()\n",
    "            train_losses.append(loss_value.cpu().item())\n",
    "            if (i + 1) % 10 == 0:\n",
    "                print('\\rTrain loss:', train_losses[-1],\n",
    "                      'Batch', i + 1, 'of', total, ' ' * 10, end='', flush=True)\n",
    "            gd.step()\n",
    "            gd.zero_grad()\n",
    "        loss_value = 0.\n",
    "        accuracy = 0.\n",
    "        for i, (x, y) in enumerate(test_dataloader):\n",
    "            total = len(test_dataloader)\n",
    "            loss_value += loss(x, torch.zeros((y.shape[0], 10)), y_encoder, z_encoder, decoder).item()\n",
    "            accuracy += (torch.argmax(y_encoder(x), 1) == y).double().mean().item()\n",
    "        print('Test loss: {}\\t Test accuracy: {}'.format(loss_value / total, accuracy / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: -172.78021240234375 Batch 600 of 600           loss: -122.69620330810547\n",
      "accuracy: 0.9350999999999999\n",
      "Train loss: -152.24264526367188 Batch 600 of 600           loss: -119.3888247680664\n",
      "accuracy: 0.9335999999999999\n",
      "Train loss: -123.75426483154297 Batch 600 of 600           loss: -117.2235368347168\n",
      "accuracy: 0.9357000000000003\n",
      "Train loss: -115.43663024902344 Batch 600 of 600           loss: -115.44101669311523\n",
      "accuracy: 0.9415999999999997\n",
      "Train loss: -139.0586700439453 Batch 600 of 600            loss: -114.25745506286621\n",
      "accuracy: 0.9462999999999999\n",
      "Train loss: -111.40328979492188 Batch 600 of 600           loss: -111.90113494873047\n",
      "accuracy: 0.9501000000000001\n",
      "Train loss: -116.46405029296875 Batch 600 of 600           loss: -110.81942817687988\n",
      "accuracy: 0.9493000000000003\n",
      "Train loss: -106.52008819580078 Batch 600 of 600           loss: -110.01765899658203\n",
      "accuracy: 0.9529999999999998\n",
      "Train loss: -110.87140655517578 Batch 600 of 600           loss: -109.4703133392334\n",
      "accuracy: 0.9514000000000001\n",
      "Train loss: -108.65312194824219 Batch 600 of 600           loss: -109.2832381439209\n",
      "accuracy: 0.9511\n",
      "Train loss: -106.08203887939453 Batch 600 of 600           loss: -108.9148828125\n",
      "accuracy: 0.9512999999999999\n",
      "Train loss: -107.08905792236328 Batch 600 of 600           loss: -108.62268562316895\n",
      "accuracy: 0.9519999999999996\n",
      "Train loss: -107.67256164550781 Batch 600 of 600           loss: -107.6027628326416\n",
      "accuracy: 0.9556999999999998\n",
      "Train loss: -137.70228576660156 Batch 600 of 600           loss: -111.36367736816406\n",
      "accuracy: 0.9274000000000006\n",
      "Train loss: -105.82194519042969 Batch 600 of 600           loss: -107.28144607543945\n",
      "accuracy: 0.9540999999999995\n",
      "Train loss: -106.03779602050781 Batch 600 of 600           loss: -106.79525505065918\n",
      "accuracy: 0.9565999999999995\n"
     ]
    }
   ],
   "source": [
    "# my implementation omitted log p(y) for observed variables. it has\n",
    "# test loss -106.79\n",
    "# test accuracy 0.95\n",
    "train_model(y_enc, z_enc, yz_dec, num_epochs=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations\n",
    "\n",
    "Generate 10 images for each label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_samples_with_fixed_classes(dec):\n",
    "    decoder_input = torch.cat((torch.eye(10).repeat(10, 1), torch.randn(100, d)), 1)\n",
    "    images = nn.functional.sigmoid(dec(decoder_input)).view(100, 28, 28).data.numpy()\n",
    "    \n",
    "    fig, axes = plt.subplots(nrows=10, ncols=10, figsize=(14, 14),\n",
    "                             subplot_kw={'xticks': [], 'yticks': []})\n",
    "    for i in range(10):\n",
    "        axes[0, i].set_title('{}'.format(i))\n",
    "    \n",
    "    for i in range(100):\n",
    "        axes[int(i / 10), i % 10].imshow(images[i], cmap='gray')\n",
    "        \n",
    "plot_samples_with_fixed_classes(yz_dec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"Style-transfer\"\n",
    "\n",
    "Here we infer latent representation $z$ of a given digit $x$ and then generate from $p(x | z, y)$ for different $y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 110 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_all_digits_with_fixed_style(z_enc, y_enc, dec):\n",
    "    indices = np.random.choice(10000, 10)\n",
    "    x, y = test_data[indices][0], torch.eye(10)[test_data[indices][1]]\n",
    "    z = z_enc(torch.cat((x, y), 1))[:, :d]\n",
    "\n",
    "    # generate digits\n",
    "    images = []\n",
    "    for i in range(10):\n",
    "        digit_encodings = torch.eye(10)[i, :].expand(10, 10)\n",
    "        images.append(nn.functional.sigmoid(dec(torch.cat((digit_encodings, z), 1)).view(10, 28, 28)).data.numpy())\n",
    "        \n",
    "    x = x.view(10, 28, 28).numpy()\n",
    "\n",
    "    # plot\n",
    "    fig, axes = plt.subplots(nrows=10, ncols=11, figsize=(14, 14),\n",
    "                             subplot_kw={'xticks': [], 'yticks': []})\n",
    "    \n",
    "    axes[0, 0].set_title('example')\n",
    "    for i in range(10):\n",
    "        axes[0, i + 1].set_title('{}'.format(i))\n",
    "        axes[i, 0].imshow(x[i], cmap='gray')\n",
    "        for j in range(10):\n",
    "            axes[i, j + 1].imshow(images[j][i], cmap='gray')\n",
    "            \n",
    "plot_all_digits_with_fixed_style(z_enc, y_enc, yz_dec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T-SNE for SS-VAE\n",
    "\n",
    "Do you notice any difference from T-SNE for vanilla VAE? How can you interpret the results?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# T-SNE for q(z | x, y) mean\n",
    "labels = test_data[:1000][1].numpy()\n",
    "encoder_input = torch.cat((test_data[:1000][0], torch.eye(10)[labels]), 1)\n",
    "latent_variables = z_enc(encoder_input)[:, :d]\n",
    "latent_variables = latent_variables.data.numpy()\n",
    "\n",
    "plot_tsne(latent_variables, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# T-SNE for q(y | x) logits\n",
    "labels = test_data[:1000][1].numpy()\n",
    "latent_variables = y_enc(test_data[:1000][0])\n",
    "latent_variables = latent_variables.data.numpy()\n",
    "\n",
    "plot_tsne(latent_variables, labels)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
